Application of mass balance models and the chemical activity concept to facilitate the use of in vitro toxicity data for risk assessment

A workflow to practically apply true dose considerations to in vitro testing for next generation risk assessment

With the move away from safety testing assessment based on data generated in experimental animals the concept of Next Generation Risk Assessment (NGRA) has arisen which instead uses data from in silico and in vitro models. A key uncertainty in risk assessment is the actual dose of test chemical at the target site, and therefore surrogate dose metrics, such as nominal concentration in test media are used to describe in vitro effect (or no-effect) doses. The reliability and accuracy of the risk assessment therefore depends largely on our ability to understand and characterise the relationship between the dose metrics used and the actual biologically effective dose at the target site. The objective of this publication is to use 40 case study chemicals to illustrate how in vitro dose considerations can be applied to characterise the "true dose" and build confidence in the understanding of the biologically effective dose in in vitro test systems for the determination e.g. points of departure (PoDs) for NGRA. We propose a workflow that can be applied to assess whether the nominal test concentration can be considered a conservative dose metric for use in NGRA. The workflow examines the implications of volatility, stability, hydrophobicity, binding to plastic and serum, solubility, and the potential use of in silico models for some of these parameters. For the majority of the case study chemicals we found that the use of nominal concentrations in risk assessment would result in conservative decision making. However, for serval chemicals a potential for underestimation of the risk in humans in vivo based on in vitro nominal effect concentrations was identified, and approaches for refinement by characterisation of the actual effect concentration are proposed.

Identifying uncertainty in physical-chemical property estimation with IFSQSAR

This study describes the development and evaluation of six new models for predicting physical-chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol-water (KOW), octanol-air (KOA), and air-water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure-Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for "novel chemicals" in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7-1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.

A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS)

Per- and polyfluoroalkyl substances (PFAS) are widely used, and their fluorinated state contributes to unique uses and stability but also long half-lives in the environment and humans. PFAS have been shown to be toxic, leading to immunosuppression, cancer, and other adverse health outcomes. Only a small fraction of the PFAS in commerce have been evaluated for toxicity using in vivo tests, which leads to a need to prioritize which compounds to examine further. Here, we demonstrate a prioritization approach that combines human biomonitoring data (blood concentrations) with bioactivity data (concentrations at which bioactivity is observed in vitro) for 31 PFAS. The in vitro data are taken from a battery of cell-based assays, mostly run on human cells. The result is a Bioactive Concentration to Blood Concentration Ratio (BCBCR), similar to a margin of exposure (MoE). Chemicals with low BCBCR values could then be prioritized for further risk assessment. Using this method, two of the PFAS, PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonic Acid), have BCBCR values < 1 for some populations. An additional 9 PFAS have BCBCR values < 100 for some populations. This study shows a promising approach to screening level risk assessments of compounds such as PFAS that are long-lived in humans and other species.

A novel method to derive a human safety limit for PFOA by gene expression profiling and modelling

Perfluorooctanoic acid (PFOA) is a persistent environmental contaminant that can accumulate in the human body due to its long half-life. This substance has been associated with liver, pancreatic, testicular and breast cancers, liver steatosis and endocrine disruption. PFOA is a member of a large group of substances also known as "forever chemicals" and the vast majority of substances of this group lack toxicological data that would enable their effective risk assessment in terms of human health hazards. This study aimed to derive a health-based guidance value for PFOA intake (ng/kg BW/day) from in vitro transcriptomics data. To this end, we developed an in silico workflow comprising five components: (i) sourcing in vitro hepatic transcriptomics concentration-response data; (ii) deriving molecular points of departure using BMDExpress3 and performing pathway analysis using gene set enrichment analysis (GSEA) to identify the most sensitive molecular pathways to PFOA exposure; (iii) estimating freely-dissolved PFOA concentrations in vitro using a mass balance model; (iv) estimating in vivo doses by reverse dosimetry using a PBK model for PFOA as part of a quantitative in vitro to in vivo extrapolation (QIVIVE) algorithm; and (v) calculating a tolerable daily intake (TDI) for PFOA. Fourteen percent of interrogated genes exhibited in vitro concentration-response relationships. GSEA pathway enrichment analysis revealed that "fatty acid metabolism" was the most sensitive pathway to PFOA exposure. In vitro free PFOA concentrations were calculated to be 2.9% of the nominal applied concentrations, and these free concentrations were input into the QIVIVE workflow. Exposure doses for a virtual population of 3,000 individuals were estimated, from which a TDI of 0.15 ng/kg BW/day for PFOA was calculated using the benchmark dose modelling software, PROAST. This TDI is comparable to previously published values of 1.16, 0.69, and 0.86 ng/kg BW/day by the European Food Safety Authority. In conclusion, this study demonstrates the combined utility of an "omics"-derived molecular point of departure and in silico QIVIVE workflow for setting health-based guidance values in anticipation of the acceptance of in vitro concentration-response molecular measurements in chemical risk assessment.

Using in vitro data to derive acceptable exposure levels: A case study on PBDE developmental neurotoxicity

BACKGROUND

Current acceptable chemical exposure levels (e.g., tolerable daily intake) are mainly based on animal experiments, which are costly, time-consuming, considered non-ethical by many, and may poorly predict adverse outcomes in humans.

OBJECTIVE

To evaluate a method using human in vitro data and biological modeling to calculate an acceptable exposure level through a case study on 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) developmental neurotoxicity (DNT).

METHODS

We reviewed the literature on in vitro assays studying BDE-47-induced DNT. Using the most sensitive endpoint, we derived a point of departure using a mass-balance in vitro disposition model and benchmark dose modeling for a 5% response (BMC05) in cells. We subsequently used a pharmacokinetic model of gestation and lactation to estimate administered equivalent doses leading to four different metrics of child brain concentration (i.e., average prenatal, average postnatal, average overall, and maximum concentration) equal to the point of departure. The administered equivalent doses were translated into tolerable daily intakes using uncertainty factors. Finally, we calculated biomonitoring equivalents for maternal serum and compared them to published epidemiological studies of DNT.

RESULTS

We calculated a BMC05 of 164 μg/kg of cells for BDE-47 induced alteration of differentiation in neural progenitor cells. We estimated administered equivalent doses of 0.925-3.767 μg/kg/day in mothers, and tolerable daily intakes of 0.009-0.038 μg/kg/day (composite uncertainty factor: 100). The lowest derived biomonitoring equivalent was 19.75 ng/g lipids, which was consistent with reported median (0.9-23 ng/g lipids) and geometric mean (7.02-26.9 ng/g lipids) maternal serum concentrations from epidemiological studies.

CONCLUSION

This case study supports using in vitro data and biological modeling as a viable alternative to animal testing to derive acceptable exposure levels.

Interpretation of in vitro concentration-response data for risk assessment and regulatory decision-making: Report from the 2022 IWGT quantitative analysis expert working group meeting

Quantitative risk assessments of chemicals are routinely performed using in vivo data from rodents; however, there is growing recognition that non-animal approaches can be human-relevant alternatives. There is an urgent need to build confidence in non-animal alternatives given the international support to reduce the use of animals in toxicity testing where possible. In order for scientists and risk assessors to prepare for this paradigm shift in toxicity assessment, standardization and consensus on in vitro testing strategies and data interpretation will need to be established. To address this issue, an Expert Working Group (EWG) of the 8th International Workshop on Genotoxicity Testing (IWGT) evaluated the utility of quantitative in vitro genotoxicity concentration-response data for risk assessment. The EWG first evaluated available in vitro methodologies and then examined the variability and maximal response of in vitro tests to estimate biologically relevant values for the critical effect sizes considered adverse or unacceptable. Next, the EWG reviewed the approaches and computational models employed to provide human-relevant dose context to in vitro data. Lastly, the EWG evaluated risk assessment applications for which in vitro data are ready for use and applications where further work is required. The EWG concluded that in vitro genotoxicity concentration-response data can be interpreted in a risk assessment context. However, prior to routine use in regulatory settings, further research will be required to address the remaining uncertainties and limitations.

Animal-free assessment of developmental toxicity: Combining PBPK modeling with the ReproTracker assay

in vitro screening platforms to assess teratogenic potential of compounds are emerging rapidly. ReproTracker is a human induced pluripotent stem cells (hiPSCs)-based biomarker assay that is shown to identify the teratogenicity potential of new pharmaceuticals and chemicals reliably. In its current state, the assay is limited to identifying the potential teratogenic effects and does not immediately quantify a clinical dose relevant to the exposure of chemicals or drugs observable in mothers or fetuses. The goal of this study was to evaluate whether the ReproTracker assay can be extrapolated in vivo and quantitatively predict developmental toxicity exposure levels of two known human teratogens, thalidomide, and carbamazepine. Here, we utilized Physiologically Based Pharmacokinetic (PBPK) modeling to describe the pharmacokinetic behavior of these compounds and conducted an in vitro to in vivo extrapolation (IVIVE) approach to predict human equivalent effect doses (HEDs) that correspond with in vitro concentrations potentially associated with adverse outcomes in ReproTracker. The HEDs derived from the ReproTracker concentration predicted to cause developmental toxicity were close to the reported teratogenic human clinical doses and the HED derived from the rat or rabbit developmental toxicity study. The ReproTracker derived-HED revealed to be sensitive and protective of humans. Overall, this pilot study demonstrated the importance of integrating PBPK model in extrapolating and assessing developmental toxicity in vitro. The combination of these tools demonstrated that they could improve the safety assessment of drugs and chemicals without animal testing.

Physiology-informed toxicokinetic model for the zebrafish embryo test developed for bisphenols

Zebrafish embryos (ZFE) is a widely used model organism, employed in various research fields including toxicology to assess e.g., developmental toxicity and endocrine disruption. Variation in effects between chemicals are difficult to compare using nominal dose as toxicokinetic properties may vary. Toxicokinetic (TK) modeling is a means to estimate internal exposure concentration or dose at target and to enable extrapolation between experimental conditions and species, thereby improving hazard assessment of potential pollutants. In this study we advance currently existing TK models for ZFE with physiological ZFE parameters and novel experimental bisphenol data, a class of chemicals with suspected endocrine activity. We developed a five-compartment model consisting of water, plastic, chorion, yolk sack and embryo in which surface area and volume changes as well as the processes of biotransformation and blood circulation influence mass fluxes. For model training and validation, we measured internal concentrations in ZFE exposed individually to BPA, bisphenol AF (BPAF) and Z (BPZ). Bayesian inference was applied for parameter calibration based on the training data set of BPZ. The calibrated TK model predicted internal ZFE concentrations of the majority of external test data within a 5-fold error and half of the data within a 2-fold error for bisphenols A, AF, F, and tetrabromo bisphenol A (TBBPA). We used the developed model to rank the hazard of seven bisphenols based on predicted internal concentrations and measured in vitro estrogenicity. This ranking indicated a higher hazard for BPAF, BPZ, bisphenol B and C (BPB, BPC) than for BPA.

Key challenges for in vitro testing of tobacco products for regulatory applications: Recommendations for dosimetry

The Institute for In Vitro Sciences (IIVS) is sponsoring a series of workshops to develop recommendations for optimal scientific and technical approaches for conducting in vitro assays to assess potential toxicity within and across tobacco and various next-generation products (NGPs) including heated tobacco products (HTPs) and electronic nicotine delivery systems (ENDSs). This publication was developed by a working group of the workshop members in conjunction with the sixth workshop in that series entitled "Dosimetry for conducting in vitro evaluations" and focuses on aerosol dosimetry for aerosol exposure to combustible cigarettes, HTP, and ENDS aerosolized tobacco products and summarizes the key challenges as well as documenting areas for future research.

Determining the toxicity of organic compounds to the nematode Caenorhabditis elegans based on aqueous concentrations

Caenorhabditis elegans is used for assessing the toxicity of chemicals in aqueous medium. However, chemicals can absorb to the bacterial food, which reduces the freely dissolved concentrations of the tested compounds. Thus, based on total or nominal concentrations, toxicity is underestimated, resulting in misleading assumptions on toxicity mechanisms or comparisons to other test organisms. As the verification of freely dissolved exposure concentrations (Cfree) is challenging in small test systems, simple partitioning models might by a good option for estimating Cfree. Therefore, C. elegans was exposed to seven differently acting organic chemicals with varying hydrophobicities, thus also different affinities to bind to the food of C. elegans. Measured concentrations of the dissolved aqueous and the bacterial-bound fraction allowed the calculation of binding constants (Kb). Experimental Kb were comparable to literature data of hydrophobic chemicals and correlated well with their hydrophobicity, expressed as log KOW. The chronic toxicity of the various compounds on C. elegans' reproduction, based on their aqueous concentration, was weakly related to their log KOW. Toxicity expressed based on chemical activity and comparisons with a baseline toxicity model, nevertheless, suggested a narcotic mode of action for most hydrophobic compounds (except methylisothiazolinone and trichlorocarbanilide). Although revealing a similar toxicity ranking than Daphnia magna, C. elegans was less sensitive, probably due to its ability to reduce its internal concentrations by means of its very impermeable cuticle or by efficient detoxification mechanisms. It could be shown that measured aqueous concentrations in the nematode test system corresponded well with freely dissolved concentrations that were modeled using simple mass-balance models from nominal concentrations. This offers the possibility to estimate freely dissolved concentrations of chemicals from nominal concentrations, making routine testing of chemicals and their comparison to other species more accurate.

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.

Quantitative in vitro to in vivo extrapolation of genotoxicity data provides protective estimates of in vivo dose

Genotoxicity assessment is a critical component in the development and evaluation of chemicals. Traditional genotoxicity assays (i.e., mutagenicity, clastogenicity, and aneugenicity) have been limited to dichotomous hazard classification, while other toxicity endpoints are assessed through quantitative determination of points-of-departures (PODs) for setting exposure limits. The more recent higher-throughput in vitro genotoxicity assays, many of which also provide mechanistic information, offer a powerful approach for determining defined PODs for potency ranking and risk assessment. In order to obtain relevant human dose context from the in vitro assays, in vitro to in vivo extrapolation (IVIVE) models are required to determine what dose would elicit a concentration in the body demonstrated to be genotoxic using in vitro assays. Previous work has demonstrated that application of IVIVE models to in vitro bioactivity data can provide PODs that are protective of human health, but there has been no evaluation of how these models perform with in vitro genotoxicity data. Thus, the Genetic Toxicology Technical Committee, under the Health and Environmental Sciences Institute, conducted a case study on 31 reference chemicals to evaluate the performance of IVIVE application to genotoxicity data. The results demonstrate that for most chemicals considered here (20/31), the PODs derived from in vitro data and IVIVE are health protective relative to in vivo PODs from animal studies. PODs were also protective by assay target: mutations (8/13 chemicals), micronuclei (9/12), and aneugenicity markers (4/4). It is envisioned that this novel testing strategy could enhance prioritization, rapid screening, and risk assessment of genotoxic chemicals.

Baseline Narcosis for the Glass-Vial 96-h Growth Inhibition of the Nematode C. elegans and Its Use for Identifying Electrophilic and Pro-Electrophilic Toxicity

The nematode Caenorhabditis elegans has been widely used as a model organism for assessing chemical toxicity. So far, however, a respective baseline narcosis reference has been lacking to predict narcosis-level toxicity and to identify excess-toxic compounds and associated mechanisms of action. Employing 22 organic narcotics that cover 7.2 units of their log K_ow (octanol/water partition coefficient) from -1.20 to 6.03, a baseline narcosis model has been derived for a glass-vial 96-h growth inhibition test with C. elegans, both without and with correction for compound loss through volatilization and sorption. The resultant effective concentrations yielding 50% growth inhibition, EC₅₀, vary by 6.4 log units from 5.04 · 10^-1 to 1.90 · 10^-7 mol/L (exposure-corrected). Application of the new model is illustrated through sensing the toxicity enhancement (T_e) of four Michael-acceptor carbonyls driven by their reactive mode of action. Moreover, narcosis-level predicted vs experimental EC₅₀ of two α,β-unsaturated alcohols demonstrate the biotransformation capability of C. elegans regarding ADH (alcohol dehydrogenase). The discussion includes narcosis-level and excess-toxicity doses (critical body burdens) as well as chemical activities A₅₀ (at the EC₅₀) as compared to fish, daphnids, ciliates, bacteria, zebrafish embryo, and cell lines. Overall, the presently introduced model for predicting C. elegans baseline narcosis enables generating respective pre-test expectations, enriches experimental results by mechanistic information, and may complement 3Rs (reduce, refine, replace) test batteries through its ADH metabolic capacity.

A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t_½) have been observed in some cases. Knowledge of chemical-specific t_½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t_½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t_½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t_½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t_½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line

With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical.

Toward Realistic Dosimetry In Vitro: Determining Effective Concentrations of Test Substances in Cell Culture and Their Prediction by an In Silico Mass Balance Model

Nominal concentrations (C_Nom) in cell culture media are routinely used to define concentration-effect relationships in the in vitro toxicology. The actual concentration in the medium (C_Medium) can be affected by adsorption processes, evaporation, or degradation of chemicals. Therefore, we measured the total and free concentration of 12 chemicals, covering a wide range of lipophilicity (log K_OW -0.07-6.84), in the culture medium (C_Medium) and cells (C_Cell) after incubation with Balb/c 3T3 cells for up to 48 h. Measured values were compared to predictions using an as yet unpublished in silico mass balance model that combined relevant equations from similar models published by others. The total C_Medium for all chemicals except tamoxifen (TAM) were similar to the C_Nom. This was attributed to the cellular uptake of TAM and accumulation into lysosomes. The free (i.e., unbound) C_Medium for the low/no protein binding chemicals were similar to the C_Nom, whereas values of all moderately to highly protein-bound chemicals were less than 30% of the C_Nom. Of the 12 chemicals, the two most hydrophilic chemicals, acetaminophen (APAP) and caffeine (CAF), were the only ones for which the C_Cell was the same as the C_Nom. The C_Cell for all other chemicals tended to increase over time and were all 2- to 274-fold higher than C_Nom. Measurements of C_Cytosol, using a digitonin method to release cytosol, compared well with C_Cell (using a freeze-thaw method) for four chemicals (CAF, APAP, FLU, and KET), indicating that both methods could be used. The mass balance model predicted the total C_Medium within 30% of the measured values for 11 chemicals. The free C_Medium of all 12 chemicals were predicted within 3-fold of the measured values. There was a poorer prediction of C_Cell values, with a median overprediction of 3- to 4-fold. In conclusion, while the number of chemicals in the study is limited, it demonstrates the large differences between C_Nom and total and free C_Medium and C_Cell, which were also relatively well predicted by the mass balance model.

Developing an internal threshold of toxicological concern (iTTC)

BACKGROUND

Threshold of Toxicological Concern (TTC) approaches are used for chemical safety assessment and risk-based priority setting for data poor chemicals. TTCs are derived from in vivo No Observed Effect Level (NOEL) datasets involving an external administered dose from a single exposure route, e.g., oral intake rate. Thus, a route-specific TTC can only be compared to a route-specific exposure estimate and such TTCs cannot be used for other exposure scenarios such as aggregate exposures.

OBJECTIVE

Develop and apply a method for deriving internal TTCs (iTTCs) that can be used in chemical assessments for multiple route-specific exposures (e.g., oral, inhalation or dermal) or aggregate exposures.

METHODS

Chemical-specific toxicokinetics (TK) data and models are applied to calculate internal concentrations (whole-body and blood) from the reported administered oral dose NOELs used to derive the Munro TTCs. The new iTTCs are calculated from the 5th percentile of cumulative distributions of internal NOELs and the commonly applied uncertainty factor of 100 to extrapolate animal testing data for applications in human health assessment.

RESULTS

The new iTTCs for whole-body and blood are 0.5 nmol/kg and 0.1 nmol/L, respectively. Because the iTTCs are expressed on a molar basis they are readily converted to chemical mass iTTCs using the molar mass of the chemical of interest. For example, the median molar mass in the dataset is 220 g/mol corresponding to an iTTC of 22 ng/L-blood (22 pg/mL-blood). The iTTCs are considered broadly applicable for many organic chemicals except those that are genotoxic or acetylcholinesterase inhibitors. The new iTTCs can be compared with measured or estimated whole-body or blood exposure concentrations for chemical safety screening and priority-setting.

SIGNIFICANCE

Existing Threshold of Toxicological Concern (TTC) approaches are limited in their applications for route-specific exposure scenarios only and are not suitable for chemical risk and safety assessments under conditions of aggregate exposure. New internal Threshold of Toxicological Concern (iTTC) values are developed to address data gaps in chemical safety estimation for multi-route and aggregate exposures.

Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis

Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for >2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure-activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85-98%) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71% with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81% using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.

Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues

BACKGROUND

The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOX^qP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOX^qP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues.

METHODS

VPA and a series of structural analogues were tested with the devTOX^qP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses.

RESULTS

The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data.

CONCLUSIONS

This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOX^qP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.

NAM-based Prediction of Point-of-contact Toxicity in the Lung: A Case Example With 1,3-dichloropropene

Time, cost, ethical, and regulatory considerations surrounding in vivo testing methods render them insufficient to meet existing and future chemical safety testing demands. There is a need for the development of in vitro and in silico alternatives to replace traditional in vivo methods for inhalation toxicity assessment. Exposures of differentiated airway epithelial cultures to gases or aerosols at the air-liquid interface (ALI) can assess tissue responses and in vitro to in vivo extrapolation can align in vitro exposure levels with in-life exposures expected to give similar tissue exposures. Because the airway epithelium varies along its length, with various regions composed of different cell types, we have introduced a known toxic vapor to five human-derived, differentiated, in vitro airway epithelial cell culture models-MucilAir of nasal, tracheal, or bronchial origin, SmallAir, and EpiAlveolar-representing five regions of the airway epithelium-nasal, tracheal, bronchial, bronchiolar, and alveolar. We have monitored toxicity in these cultures 24hours after acute exposure using an assay for transepithelial conductance (for epithelial barrier integrity) and the lactate dehydrogenase (LDH) release assay (for cytotoxicity). Our vapor of choice in these experiments was 1,3-dichloropropene (1,3-DCP). Finally, we have developed an airway dosimetry model for 1,3-DCP vapor to predict in vivo external exposure scenarios that would produce toxic local tissue concentrations as determined by in vitro experiments. Measured in vitro points of departure (PoDs) for all tested cell culture models were similar. Calculated rat equivalent inhaled concentrations varied by model according to position of the modeled tissue within the airway, with nasal respiratory tissue being the most proximal and most sensitive tissue, and alveolar epithelium being the most distal and least sensitive tissue. These predictions are qualitatively in accordance with empirically determined in vivo PoDs. The predicted PoD concentrations were close to, but slightly higher than, PoDs determined by in vivo subchronic studies.

Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model

Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose−response evaluation of mixtures. We previously found, using five human cell-based assays, that concentration addition (CA), the usual default approach to calculate cumulative risk, is mostly accurate to within an order of magnitude. Here, we extend these findings to further investigate how cell-based data can be used to quantify inter-individual variability in CA. Utilizing data from testing 42 Superfund priority chemicals separately and in 8 defined mixtures in a human cell-based population-wide in vitro model, we applied CA to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio (defined as the toxicodynamic variability factor, TDVF). We quantified the accuracy of CA with the Loewe Additivity Index (LAI). We found that LAI varies more between different mixtures than between different individuals, and that predictions of the population median are generally more accurate than predictions for the “sensitive” individual or the TDVF. Moreover, LAI values were generally <1, indicating that the mixtures were more potent than predicted by CA. Together with our previous studies, we posit that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple CA models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Dynamic Mass Balance Modeling for Chemical Distribution Over Time in In Vitro Systems With Repeated Dosing

As toxicologists and risk assessors move away from animal testing and more toward using in vitro models and biological modeling, it is necessary to produce tools to quantify the chemical distribution within the in vitro environment prior to extrapolating in vitro concentrations to human equivalent doses. Although models predicting chemical distribution in vitro have been developed, very little has been done for repeated dosing scenarios, which are common in prolonged experiments where the medium needs to be refreshed. Failure to account for repeated dosing may lead to inaccurate estimations of exposure and introduce bias into subsequent in vitro to in vivo extrapolations. Our objectives were to develop a dynamic mass balance model for repeated dosing in in vitro systems; to evaluate model accuracy against experimental data; and to perform illustrative simulations to assess the impact of repeated doses on predicted cellular concentrations. A novel dynamic in vitro partitioning mass balance model (IV-MBM DP v1.0) was created based on the well-established fugacity approach. We parameterized and applied the dynamic mass balance model to single dose and repeat dosing scenarios, and evaluated the predicted medium and cellular concentrations against available empirical data. We also simulated repeated dosing scenarios for organic chemicals with a range of partitioning properties and compared the in vitro distributions over time. In single dose scenarios, for which only medium concentrations were available, simulated concentrations predicted measured concentrations with coefficients of determination (R2) of 0.85–0.89, mean absolute error within a factor of two and model bias of nearly one. Repeat dose scenario simulations displayed model bias &lt;2 within the cell lysate, and ∼1.5-3 in the medium. The concordance between simulated and available experimental data supports the predictive capacity of the IV-MBM DP v1.0 tool, but further evaluation as empirical data becomes available is warranted, especially for cellular concentrations.

The Evolution of Regulatory Toxicology: Where is the Gardener?

There is a need for paradigm change in the methodology employed for toxicological testing and assessment. It could be said that this change is well on its way, through an evolutionary progress analogous to that of natural selection. Darwin's Theory of Evolution has defined the idea of evolution and descendancy since the last third of the 19th century. Increasingly, this concept of 'evolution' is being applied beyond the field of biology. This Comment article discusses the progress of toxicological testing in the context of 'evolutionary pressure' and deliberates how this process can help foster the development, implementation and acceptance of mechanistic and human-relevant methods in this field. By comparing the current regulatory landscape in toxicity testing and assessment to specific elements in Charles Darwin's evolutionary theory, we aim to better understand the needs and requirements for the future.

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.

Comprehensive interpretation of in vitro micronucleus test results for 292 chemicals: from hazard identification to risk assessment application

Risk assessments are increasingly reliant on information from in vitro assays. The in vitro micronucleus test (MNvit) is a genotoxicity test that detects chromosomal abnormalities, including chromosome breakage (clastogenicity) and/or whole chromosome loss (aneugenicity). In this study, MNvit datasets for 292 chemicals, generated by the US EPA’s ToxCast program, were evaluated using a decision tree-based pipeline for hazard identification. Chemicals were tested with 19 concentrations (n = 1) up to 200 µM, in the presence and absence of Aroclor 1254-induced rat liver S9. To identify clastogenic chemicals, %MN values at each concentration were compared to a distribution of batch-specific solvent controls; this was followed by cytotoxicity assessment and benchmark concentration (BMC) analyses. The approach classified 157 substances as positives, 25 as negatives, and 110 as inconclusive. Using the approach described in Bryce et al. (Environ Mol Mutagen 52:280–286, 2011), we identified 15 (5%) aneugens. IVIVE (in vitro to in vivo extrapolation) was employed to convert BMCs into administered equivalent doses (AEDs). Where possible, AEDs were compared to points of departure (PODs) for traditional genotoxicity endpoints; AEDs were generally lower than PODs based on in vivo endpoints. To facilitate interpretation of in vitro MN assay concentration–response data for risk assessment, exposure estimates were utilized to calculate bioactivity exposure ratio (BER) values. BERs for 50 clastogens and two aneugens had AEDs that approached exposure estimates (i.e., BER < 100); these chemicals might be considered priorities for additional testing. This work provides a framework for the use of high-throughput in vitro genotoxicity testing for priority setting and chemical risk assessment.

U.S. Federal Agency interests and key considerations for new approach methodologies for nanomaterials

Engineered nanomaterials (ENMs) come in a wide array of shapes, sizes, surface coatings, and compositions, and often possess novel or enhanced properties compared to larger sized particles of the same elemental composition. To ensure the safe commercialization of products containing ENMs, it is important to thoroughly understand their potential risks. Given that ENMs can be created in an almost infinite number of variations, it is not feasible to conduct in vivo testing on each type of ENM. Instead, new approach methodologies (NAMs) such as in vitro or in chemico test methods may be needed, given their capacity for higher throughput testing, lower cost, and ability to provide information on toxicological mechanisms. However, the different behaviors of ENMs compared to dissolved chemicals may challenge safety testing of ENMs using NAMs. In this study, member agencies within the Interagency Coordinating Committee on the Validation of Alternative Methods were queried about what types of ENMs are of agency interest and whether there is agency-specific guidance for ENM toxicity testing. To support the ability of NAMs to provide robust results in ENM testing, two key issues in the usage of NAMs, namely dosimetry and interference/bias controls, are thoroughly discussed.

Extension of the Virtual Cell Based Assay from a 2-D to a 3-D Cell Culture Model

Prediction of chemical toxicity is very useful in risk assessment. With the current paradigm shift towards the use of in vitro and in silico systems, we present herein a theoretical mathematical description of a quasi-diffusion process to predict chemical concentrations in 3-D spheroid cell cultures. By extending a 2-D Virtual Cell Based Assay (VCBA) model into a 3-D spheroid cell model, we assume that cells are arranged in a series of concentric layers within the sphere. We formulate the chemical quasi-diffusion process by simplifying the spheroid with respect to the number of cells in each layer. The system was calibrated and tested with acetaminophen (APAP). Simulated predictions of APAP toxicity were compared with empirical data from in vitro measurements by using a 3-D spheroid model. The results of this first attempt to extend the VCBA model are promising - they show that the VCBA model simulates close correlation between the influence of compound concentration and the viability of the HepaRG 3-D cell culture. The 3-D VCBA model provides a complement to current in vitro procedures to refine experimental setups, to fill data gaps and help in the interpretation of in vitro data for the purposes of risk assessment.

Use of in vitro metabolism and biokinetics assays to refine predicted in vivo and in vitro internal exposure to the cosmetic ingredient, phenoxyethanol, for use in risk assessment

A novel approach was developed to help characterize the biokinetics of the cosmetic ingredient, phenoxyethanol, to help assess the safety of the parent and its major stable metabolite. In the first step of this non-animal tiered approach, primary human hepatocytes were used to confirm or refute in silico predicted metabolites, and elucidate the intrinsic clearance of phenoxyethanol. A key result was the identification of the major metabolite, phenoxyacetic acid (PAA), the exposure to which in the kidney was subsequently predicted to far exceed that of phenoxyethanol in blood or other tissues. Therefore, a novel aspect of this approach was to measure in the subsequent step the formation of PAA in the cells dosed with phenoxyethanol that were used to provide points of departure (PoDs) and express the intracellular exposure as the Cmax and AUC24. This enabled the calculation of the intracellular concentrations of parent and metabolite at the PoD in the cells used to derive this value. These concentrations can be compared with in vivo tissue levels to conclude on the safety margin. The lessons from this case study will help to inform the design of other non-animal safety assessments.

Derivation of a Human In Vivo Benchmark Dose for Bisphenol A from ToxCast In Vitro Concentration Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

A computational workflow which integrates physiologically based kinetic (PBK) modelling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC), Markov Chain Monte Carlo (MCMC) simulation and the Virtual Cell Based Assay (VCBA) for the estimation of the active, free in vitro concentration of chemical in the reaction medium was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow was designed to estimate parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for bisphenol A (BPA) and high throughput screening (HTS) in vitro concentration-response data, for estrogen and pregnane X receptor activation determined in human liver and kidney cell lines, from the ToxCast/Tox21 database. In vivo benchmark dose 10% lower confidence limits (BMDL₁₀) for oral uptake of BPA (ng/kg BW/day) were calculated from the in vivo dose-responses and compared to the human equivalent dose (HED) BMDL₁₀ for relative kidney weight change in the mouse derived by European Food Safety Authority (EFSA). Three from four in vivo BMDL₁₀ values calculated in this study were similar to the EFSA values whereas the fourth was much smaller. The derivation of an uncertainty factor (UF) to accommodate the uncertainties associated with measurements using human cell lines in vitro, extrapolated to in vivo, could be useful for the derivation of Health Based Guidance Values (HBGV).

Update and Evaluation of a High-Throughput In Vitro Mass Balance Distribution Model: IV-MBM EQP v2.0

This study demonstrates the utility of an updated mass balance model for predicting the distribution of organic chemicals in in vitro test systems (IV-MBM EQP v2.0) and evaluates its performance with empirical data. The IV-MBM EQP v2.0 tool was parameterized and applied to four independent data sets with measured ratios of bulk medium or freely-dissolved to initial nominal concentrations (e.g., C24/C0 where C24 is the measured concentration after 24 h of exposure and C0 is the initial nominal concentration). Model performance varied depending on the data set, chemical properties (e.g., "volatiles" vs. "non-volatiles", neutral vs. ionizable organics), and model assumptions but overall is deemed acceptable. For example, the r² was greater than 0.8 and the mean absolute error (MAE) in the predictions was less than a factor of two for most neutral organics included. Model performance was not as good for the ionizable organic chemicals included but the r² was still greater than 0.7 and the MAE less than a factor of three. The IV-MBM EQP v2.0 model was subsequently applied to several hundred chemicals on Canada's Domestic Substances List (DSL) with nominal effects data (AC50s) reported for two in vitro assays. We report the frequency of chemicals with AC50s corresponding to predicted cell membrane concentrations in the baseline toxicity range (i.e., >20-60 mM) and tabulate the number of chemicals with "volatility issues" (majority of chemical in headspace) and "solubility issues" (freely-dissolved concentration greater than water solubility after distribution). In addition, the predicted "equivalent EQP blood concentrations" (i.e., blood concentration at equilibrium with predicted cellular concentration) were compared to the AC50s as a function of hydrophobicity (log octanol-water partition or distribution ratio). The predicted equivalent EQP blood concentrations exceed the AC50 by up to a factor of 100 depending on hydrophobicity and assay conditions. The implications of using AC50s as direct surrogates for human blood concentrations when estimating the oral equivalent doses using a toxicokinetic model (i.e., reverse dosimetry) are then briefly discussed.

Differences in marine megafauna in vitro sensitivity highlights the need for species-specific chemical risk assessments

Sea turtles, dolphins and dugongs can be exposed to large mixtures of contaminants due to the proximity of foraging locations to anthropogenic inputs. Differences in accumulation and effect result in differences of chemical risk to these species. However, little is known about the effect of contaminants in marine wildlife. Cell-based, or in vitro, exposure experiments offer an ethical alternative to investigate the effect of contaminants in wildlife. Data from in vitro studies can then be placed in an environmental context, by using screening risk assessments, comparing effect data with accumulation data from the literature, to identify risk to populations of marine wildlife. Cytotoxicity of Cr⁶⁺, Cd²⁺, Hg²⁺, 4,4'-DDE, and PFNA were investigated in primary skin fibroblasts of green turtles, loggerhead turtles, hawksbill turtles, dugongs, Burrunan dolphins, and common bottlenose dolphins. The general order of toxicity for all species was Hg²⁺> Cr⁶⁺ > Cd²⁺> 4,4'-DDE > PFNA, and significant differences in cytotoxicity were found between species for Cr⁶⁺, Cd²⁺ and PFNA. For Cd²⁺, in particular, cells from turtle species were less sensitive than mammalian species, and dugong cells were by far the most sensitive. The results from the cytotoxicity assay were then used in combination with published data on tissue contaminant concentrations to calculate risk quotients for identifying populations of each species most at risk from these chemicals. Cr, Cd and Hg were identified as posing risk in all six species. Dugongs were particularly at risk from Cd accumulation and dolphin species were particularly at risk from Hg accumulation. These results demonstrate the importance of using species-specific effect and accumulation data for developing chemical risk assessments and can be used to inform managers of priority contaminants, species, or populations. Development of additional in vitro endpoints, and improving links between in vitro and in vivo effects, would further improve this approach to understanding chemical risk in marine megafauna.

Mind the Exposure Gaps-Modeling Chemical Transport in Sediment Toxicity Tests

Chemical exposure in flow-through sediment toxicity tests can vary in time, between pore and overlying water, and amid free and bound states, complicating the link between toxicity and observable concentrations such as free pore (C_free,pore), free overlying (C_free,over), or the corresponding dissolved concentrations (C_diss, free + bound to dissolved organic carbon, DOC). We introduce a numerical model that describes the desorption from sediments to pore water, diffusion through pores and the sediment-water boundary, DOC-mediated transport, and mixing in and outflow from overlying water. The model explained both the experimentally measured gap between C_free,over and C_free,pore and the continuous decrease in overlying C_diss. Spatially resolved modeling suggested a steep concentration gradient present in the upper millimeter of the sediment due to slow chemical diffusion in sediment pores and fast outflux from the overlying water. In contrast to continuous decrease in overlying C_diss expected for any chemical, C_free,over of highly hydrophobic chemicals was kept relatively constant following desorption from DOC, a mechanism comparable to passive dosing. Our mechanistic analyses emphasize that exposure will depend on the chemical's hydrophobicity, the test organism habitat and uptake of bound chemicals, and the properties of sediment components, including DOC. The model can help to re-evaluate existing toxicity data, optimize experimental setups, and extrapolate laboratory toxicity data to field exposure.

Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: Implications for model selection and tiered modeling frameworks

This study describes the development and intercomparison of generic physiologically-based toxicokinetic (PBTK) models for humans comprised of internally consistent one-compartment (1Co-) and multi-compartment (MCo-) implementations (G-PBTK). The G-PBTK models were parameterized for an adult male (70 kg) using common physiological parameters and in vitro biotransformation rate estimates and subsequently evaluated using independent concentration versus time data (n = 6) and total elimination half-lives (n = 15) for diverse organic chemicals. The model performance is acceptable considering the inherent uncertainty in the biotransformation rate data and the absence of model calibration. The G-PBTK model was then applied using hypothetical neutral organics, acidic ionizable organics and basic ionizable organics (IOCs) to identify combinations of partitioning properties and biotransformation rates leading to substantial discrepancies between 1Co- and MCo-PBTK calculations for whole body concentrations and half-lives. The 1Co- and MCo-PBTK model calculations for key toxicokinetic parameters are broadly consistent unless biotransformation is rapid (e.g., half-life less than five days). When half-lives are relatively short, discrepancies are greatest for the neutral organics and least for the acidic IOCs which follows from the estimated volumes of distribution (e.g., VD_SS = 9.6-15.4 L/kg vs 0.3-1.6 L/kg for the neutral and acidic compounds respectively) and the related approach to internal chemical equilibrium. The model intercomparisons demonstrate that 1Co-PBTK models can be applied with confidence to many exposure scenarios, particularly those focused on chronic or repeat exposures and for prioritization and screening-level decision contexts. However, MCo-PBTK models may be necessary in certain contexts, particularly for intermittent, short-term and highly variable exposures. A key recommendation to guide model selection and the development of tiered PBTK modeling frameworks that emerges from this study is the need to harmonize models with respect to parameterization and process descriptions to the greatest extent possible when proceeding from the application of simpler to more complex modeling tools as part of chemical assessment activities.

Critical Membrane Concentration and Mass-Balance Model to Identify Baseline Cytotoxicity of Hydrophobic and Ionizable Organic Chemicals in Mammalian Cell Lines

All chemicals can interfere with cellular membranes and this leads to baseline toxicity, which is the minimal toxicity any chemical elicits. The critical membrane burden is constant for all chemicals; that is, the dosing concentrations to trigger baseline toxicity decrease with increasing hydrophobicity of the chemicals. Quantitative structure-activity relationships, based on hydrophobicity of chemicals, have been established to predict nominal concentrations causing baseline toxicity in human and mammalian cell lines. However, their applicability is limited to hydrophilic neutral compounds. To develop a prediction model that includes more hydrophobic and charged organic chemicals, a mass balance model was applied for mammalian cells (AREc32, AhR-CALUX, PPARγ-BLA, and SH-SY5Y) considering different bioassay conditions. The critical membrane burden for baseline toxicity was converted into nominal concentration causing 10% cytotoxicity by baseline toxicity (IC_10,baseline) using a mass balance model whose main chemical input parameter was the liposome-water partition constants (K_lip/w) for neutral chemicals or the speciation-corrected D_lip/w(pH 7.4) for ionizable chemicals plus the bioassay-specific protein, lipid, and water contents of cells and media. In these bioassay-specific models, log(1/IC_10,baseline) increased with increasing hydrophobicity, and the relationship started to level off at log D_lip/w around 2. The bioassay-specific models were applied to 392 chemicals covering a broad range of hydrophobicity and speciation. Comparing the predicted IC_10,baseline and experimental cytotoxicity IC₁₀, known baseline toxicants and many additional chemicals were identified as baseline toxicants, while the others were classified based on specificity of their modes of action in the four cell lines, confirming excess toxicity of some fungicides, antibiotics, and uncouplers. Given the similarity of the bioassay-specific models, we propose a generalized baseline-model for adherent human cell lines: log[1/IC_10,baseline (M)] = 1.23 + 4.97 × (1 - e^{-0.236 log D_lip/w}). The derived models for baseline toxicity may serve for specificity analysis in reporter gene and neurotoxicity assays as well as for planning the dosing for cell-based assays.

Identifying Chemicals and Mixtures of Potential Biological Concern Detected in Passive Samplers from Great Lakes Tributaries Using High-Throughput Data and Biological Pathways

Waterborne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs) and polar organic chemical integrative samplers (POCIS). A risk-based screening approach was used to prioritize chemicals and chemical mixtures, identify sites at greatest risk for biological impacts, and identify potential hazards to monitor at those sites. Analyses included 185 chemicals (143 detected) including polycyclic aromatic hydrocarbons (PAHs), legacy and current-use pesticides, fire retardants, pharmaceuticals, and fragrances. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (toxicity quotients) or ToxCast database (exposure-activity ratios [EARs]). Mixture effects were estimated by summation of EAR values for chemicals that influence ToxCast assays with common gene targets. Nineteen chemicals-atrazine, N,N-diethyltoluamide, di(2-ethylhexyl)phthalate, dl-menthol, galaxolide, p-tert-octylphenol, 3 organochlorine pesticides, 3 PAHs, 4 pharmaceuticals, and 3 phosphate flame retardants-had toxicity quotients >0.1 or EARs for individual chemicals >10^-3 at 10% or more of the sites monitored. An additional 4 chemicals (tributyl phosphate, triethyl citrate, benz[a]anthracene, and benzo[b]fluoranthene) were present in mixtures with EARs >10^-3 . To evaluate potential apical effects and biological endpoints to monitor in exposed wildlife, in vitro bioactivity data were compared to adverse outcome pathway gene ontology information. Endpoints and effects associated with endocrine disruption, alterations in xenobiotic metabolism, and potentially neuronal development would be relevant to monitor at the priority sites. The EAR threshold exceedance for many chemical classes was correlated with urban land cover and wastewater effluent influence, whereas herbicides and fire retardants were also correlated to agricultural land cover. Environ Toxicol Chem 2021;40:2165-2182. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Passive Sampling and Dosing of Aquatic Organic Contaminant Mixtures for Ecotoxicological Analyses

Toxicity results from exposure to mixtures of organic contaminants. Assessing this using ecotoxicity bioassays involves sampling of the environmental mixture and then introducing this into the test. The first step is accounting for the bioavailable levels of all mixture constituents. Passive sampling specifically targets these bioavailable fractions but the sampler-accumulated mixture varies with the compound and sampler properties as well as time. The second step involves reproducing and maintaining the sampled mixture constituents in the bioassay. Passive sampler extraction and spiking always leads to a skewed mixture profile in the test. Alternatively, the recovered passive samplers might be directly used in passive dosing mode. Here, the reproduced contaminant mixture depends on whether kinetic or equilibrium sampling applies. These concepts were tested for determining the combined toxicity of laboratory and field mixtures of aquatic contaminants in the Microtox and ER-Calux bioassays. Aqueous sample extraction and spiking, passive sampler extraction and spiking, and passive sampling and dosing were compared for first sampling and then introducing mixtures in toxicity bioassays. The analytical and toxicity results show that the correct way to first sample the bioavailable mixture profile, and then to reproduce and maintain this in the toxicity test, is by combining equilibrium passive sampling and dosing.

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.

Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

Gaining acceptance in next generation PBK modelling approaches for regulatory assessments - An OECD international effort

Physiologically Based Kinetic (PBK) models are valuable tools to help define safe external levels of chemicals based on internal doses at target organs in experimental animals, humans and organisms used in environmental risk assessment. As the toxicity testing paradigm shifts to alternative testing approaches, PBK model development has started to rely (mostly or entirely) on model parameters quantified using in vitro or in silico methods. Recently, the Organisation for Economic Cooperation and Development (OECD) published a guidance document (GD) describing a scientific workflow for characterising and validating PBK models developed using in vitro and in silico data. The GD provides an assessment framework for evaluating these models, with emphasis on the major uncertainties underlying model inputs and outputs. To help end-users submit or evaluate a PBK model for regulatory purposes, the GD also includes a template for documenting the model characteristics, and a checklist for evaluating the quality of a model. This commentary highlights the principles, criteria and tools laid out in the OECD PBK model GD, with the aim of facilitating the dialogue between model developers and risk assessors.

Quantitative In Vitro-to-In Vivo Extrapolation: Nominal versus Freely Dissolved Concentration

Discussions are ongoing on which dose metric should be used for quantitative in vitro-to-in vivo extrapolation (QIVIVE) of in vitro bioassay data. The nominal concentration of the test chemicals is most commonly used and easily accessible, while the concentration freely dissolved in the assay medium is considered to better reflect the bioavailable concentration but is tedious to measure. The aim of this study was to elucidate how much QIVIVE results will differ when using either nominal or freely dissolved concentrations. QIVIVE_nom and QIVIVE_free ratios, that is, the ratios of plasma concentrations divided by in vitro effect concentrations, were calculated for 10 pharmaceuticals using previously published nominal and freely dissolved effect concentrations for the activation of the peroxisome proliferator-activated receptor gamma (PPARγ) and the activation of oxidative stress response. The QIVIVE_nom ratios were higher than QIVIVE_free ratios by up to a factor of 60. The risk of in vivo effects was classified as being high or low for four chemicals using the QIVIVE_nom and for three chemicals using QIVIVE_free ratios. Unambiguous classification was possible for nine chemicals by combining the QIVIVE_nom or QIVIVE_free ratios with the respective specificity ratios (SR_nom or SR_free) of the in vitro effect data, which helps to identify whether the specific effect was influenced by cytotoxicity. QIVIVE_free models should be preferred as they account for differences in bioavailability between in vitro and in vivo, but QIVIVE_nom may still be useful for screening the effects of large numbers of chemicals because it is generally more conservative. The use of SR of the in vitro effect data as a second classification factor is recommended for QIVIVE_nom and QIVIVE_free models because a clearer picture can be obtained with respect to the likelihood that a biological effect will occur and that it is not caused by nonspecific cytotoxicity.

Assessing the Ecological Risks of Per- and Polyfluoroalkyl Substances: Current State-of-the Science and a Proposed Path Forward

Per- and poly-fluoroalkyl substances (PFAS) encompass a large, heterogenous group of chemicals of potential concern to human health and the environment. Based on information for a few relatively well-understood PFAS such as perfluorooctane sulfonate and perfluorooctanoate, there is ample basis to suspect that at least a subset can be considered persistent, bioaccumulative, and/or toxic. However, data suitable for determining risks in either prospective or retrospective assessments are lacking for the majority of PFAS. In August 2019, the Society of Environmental Toxicology and Chemistry sponsored a workshop that focused on the state-of-the-science supporting risk assessment of PFAS. The present review summarizes discussions concerning the ecotoxicology and ecological risks of PFAS. First, we summarize currently available information relevant to problem formulation/prioritization, exposure, and hazard/effects of PFAS in the context of regulatory and ecological risk assessment activities from around the world. We then describe critical gaps and uncertainties relative to ecological risk assessments for PFAS and propose approaches to address these needs. Recommendations include the development of more comprehensive monitoring programs to support exposure assessment, an emphasis on research to support the formulation of predictive models for bioaccumulation, and the development of in silico, in vitro, and in vivo methods to efficiently assess biological effects for potentially sensitive species/endpoints. Addressing needs associated with assessing the ecological risk of PFAS will require cross-disciplinary approaches that employ both conventional and new methods in an integrated, resource-effective manner. Environ Toxicol Chem 2021;40:564-605. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Predicting exposure concentrations of chemicals with a wide range of volatility and hydrophobicity in different multi-well plate set-ups

Quantification of chemical toxicity in small-scale bioassays is challenging owing to small volumes used and extensive analytical resource needs. Yet, relying on nominal concentrations for effect determination maybe erroneous because loss processes can significantly reduce the actual exposure. Mechanistic models for predicting exposure concentrations based on distribution coefficients exist but require further validation with experimental data. Here we developed a complementary empirical model framework to predict chemical medium concentrations using different well-plate formats (24/48-well), plate covers (plastic lid, or additionally aluminum foil or adhesive foil), exposure volumes, and biological entities (fish, algal cells), focusing on the chemicals’ volatility and hydrophobicity as determinants. The type of plate cover and medium volume were identified as important drivers of volatile chemical loss, which could accurately be predicted by the framework. The model focusing on adhesive foil as cover was exemplary cross-validated and extrapolated to other set-ups, specifically 6-well plates with fish cells and 24-well plates with zebrafish embryos. Two case study model applications further demonstrated the utility of the empirical model framework for toxicity predictions. Thus, our approach can significantly improve the applicability of small-scale systems by providing accurate chemical concentrations in exposure media without resource- and time-intensive analytical measurements.

High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.