Styrene and ethylbenzene are chemicals used in the plastic and rubber industry and are present in cigarette smoke. Regulatory authorities have developed toxicological reference values to conduct risk assessments for potential exposures. The extent of exposures to ST and ETB can be assessed via biomonitoring of shared metabolites mandelic acid and phenylglyoxylic acid in urine. The metabolism of ST and ETB and the excretion of MA and PGA in urine following controlled inhalation exposures in humans has been studied. In this evaluation, we derive Biomonitoring Equivalents (BEs) for interpretation of general population biomonitoring data. BEs are defined as the concentration or range of concentrations of a chemical or its metabolite in a biological medium (blood, urine, human milk, etc.) that is consistent with existing health-based TRVs such as a reference concentration. The BE values derived in this manuscript are screening values that can help public health officials and regulators interpret biomonitoring data for styrene and ethylbenzene. Examples of interpretating existing biomonitoring data available from the National Health And Nutrition Examination Survey are provided, with additional considerations given to understanding potential sources of confounding associated with body mass index and sex.

[This corrects the article on p. 1107 in vol. 25, PMID: 42517087.].

The use of phosphatidylethanol (PEth) as a biomarker for alcohol consumption is increasing likely due to its relatively long half-life in blood. Here, we present a pharmacokinetic model for three common homologs of PEth based on concentrations of each observed in a 5-day study of daily alcohol consumption. Adult participants were 11 females and 6 males with a median age of 32 years and median BMI of 24.3, all of whom drank on 1 or more days per week with at least 1 day per month of “heavy” drinking and also free from psychiatric disorders. All participants were abstinent for one week prior to beginning the study. The overall goals of this modeling effort are the use of PEth for assessment of alcohol consumption behavior and better understanding of the biological mechanisms underlying PEth pharmacokinetics. The modeling presented encompasses both the calibration of the pharmacokinetic model from daily individual PEth measurements and the prediction of model parameters in the study population with a regression model. The overall model was then evaluated by comparison of predicted PEth levels in blood with those measured in several groups of subjects in controlled drinking experiments. The results of this modeling effort indicate that the model can predict PEth concentrations in blood from alcohol consumption albeit with high variability both between individuals and within a single individual between drinking occasions. These results suggest the possible need to refine currently used cutoffs used in clinical and forensic contexts to predict alcohol consumption amounts. (249 words)

A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.

In 2022 the World Health Organization (WHO) published updated ‘Toxic Equivalence Factors’ (TEFs) for a wide variety of chlorinated dioxins, dibenzofurans and PCBs [collectively referred to as ‘dioxin-like chemicals’; DLCs) that interact with the aryl hydrocarbon receptor (AHR)]. Their update used sophisticated statistical analysis of hundreds of published studies that reported estimation of ‘Relative Effective Potency’ (REP) values for individual DLC congeners. The weighting scheme used in their assessment of each study favored in vivo over in vitro studies and was based largely on rodent studies. In this Commentary, we highlight the large body of published studies that demonstrate large species differences in AHR-ligand activation and provide supporting evidence for our position that the WHO 2022 TEF values intended for use in human risk assessment of DLC mixtures will provide highly misleading overestimates of ‘Toxic Equivalent Quotients’ (TEQs), because of well-recognized striking differences in AHR ligand affinities between rodent (rat, mouse) and human. The data reviewed in our Commentary support the position that human tissue-derived estimates of REP/TEF values for individual DLC congeners, although uncertain, will provide much better, more realistic estimates of potential activation of the human AHR, when exposure to complex DLC mixtures occurs.

The Predictive Analytics Toolkit (PAT) was developed to facilitate use of new approach methodologies (NAMs) to predict health hazards and risks from chemicals. PAT is a user-friendly web application that integrates many R packages to enable development and testing of prediction models without any programming. We drew from the work of Ring et al. 2021 (https://doi.org/10.1016/j.comtox.2021.100166), who used random forest models to predict in vivo transcriptomic responses in rat liver from in vitro Tox21 AC50 values for a set of 221 chemicals. Gene ontologies helped identify 735 biological pathways based on differential in vivo expression of specific gene sets. Ring et al. used 12 models that varied in use of toxicokinetics to predict in vivo activity using 5000 random forest iterations for each chemical/pathway combination (the area under the receiver-operator characteristic curve (AUC-ROC) was the measure of model performance). The highest-ranking model (Model 10) used Tox21 AC50 nominal concentrations converted to media concentrations and in vivo doses converted to circulating plasma concentrations; the lowest ranking model (Model 2) used nominal in vitro concentrations and adminis-tered in vivo dose levels. Using a subset of 10 pathways from the Ring et al. data, we used PAT to predict the AUC-ROC and to compare the best (Model 10) and worst (Model 2) performing models with only 100 random forest iterations. Using the results from PAT, Model 10 "won" in 60% of the comparisons, a value similar to that calculated for the identical set of comparisons using the supplemental data from Ring et al. (52.2%). Hence, PAT can provide a useful alternative to programming in R for prediction modeling and model performance evalua-tion, even for extensive genomic data sets.

Advanced test systems and knowledge of biology, how chemical exposures occur, and the mechanisms, pathways and dose-dependent changes that can lead to toxicity are rapidly catalyzing the transformation away from traditional approaches to new approach methodologies for predicting potential hazards and risks. The explicit incorporation of inference modeling as an integral component of NAMs requires evaluation and transparent documentation of inference model robustness and predictivity. The Predictive Analytics Toolkit, described herein, was developed to facilitate such analyses. PAT is a free, user-friendly, cloud-based web platform that provides automated development and testing of prediction models. PAT provides simplified access to the analytics power of a vast array of R packages for detecting, analyzing, quantifying, and visualizing associations and other relations (such as information relations among multiple variables) in user uploaded Excel datasets using standardized, well-documented, and well-supported algorithms.

Science peer review plays an important role in the advancement and acceptance of scientific information, particularly when used to support decision-making. A model for science peer review is proposed here using a large, multi-tiered case study to engage a broader segment of the scientific community to support decision making on science matters, and to incorporate many of the design advantages of the two common forms of peer review (journal peer review, science advisory panels). This peer review consisted of a two-tiered structure consisting of seven panels (five review panels in Tier 1, two review panels in Tier 2), which focused on safety data for a modified risk tobacco product (MRTP). Experts from all over the world were invited to apply to one or more positions on seven peer review panels. 66 peer reviewers were selected from available applicants using objective metrics of their expertise, and for some panels based upon a consideration of panel diversity with respect to demographic parameters (e.g., geographic region, sector of employment, years of experience). All peer reviewers participated anonymously in which a third-party auditor was used to provide independent verification of their expertise. Peer reviewers were provided electronic links to all review material which included access to publications, reports, omics data, and histopathology slides, with topic-specific panels focusing on topic-specific components of the review package. Peer reviews consisted either of single-round, or multi-round (e.g., modified Delphi) format. Peer reviewer responses to the charge questions were collected via an online survey system, and were assembled into a database. Responses in the database were subject to analyses to assess the degree of favorability (i.e., supportive of the review material), degree of consensus, reproducibility of replicate panels, hidden sources of bias, and outlier response patterns. Conclusions: By careful consideration of science peer review design elements we have shown that: 1) panel participation can be broadened to include scientists who would otherwise not participate; 2) panel diversity can be managed in an unbiased manner without adverse impacts to panel expertise; 3) results obtained from independent concurrent panels are shown to be reproducible; and 4) there are benefits of collecting input from expert panels via a structured format (i.e., survey) to support characterization of consensus, identification of hidden sources of bias, and identification of potential outlier participants.

Regulatory agencies across the world are facing the challenge of performing risk-based prioritization of thousands of chemicals in commerce. Here, we present an approach using the Threshold of Toxicological Concern (TTC) combined with heuristic high-throughput exposure (HTE) modelling to rank order chemicals for further evaluation. Accordingly, for risk-based prioritization, chemicals with exposures > TTC would be ranked as higher priority for further evaluation whereas substances with exposures < TTC would be ranked as lower priority. An initial proof of concept, using a dataset of 7986 substances with previously modeled median and upper 95% credible interval (UCI) total daily median exposure rates showed fewer than 5% of substances had UCI exposures > the Cramer Class III TTC (1.5 μg/kg-day). We extended the analysis by profiling the same dataset through the TTC workflow published by Kroes et al (2004) which accounts for known exclusions to the TTC as well as structural alerts. UCI exposures were then compared to the appropriate class-specific TTC. None of the substances categorized as Cramer Class I or Cramer Class II exceeded their respective TTC values and no more than 2% of substances categorized as Cramer Class III or acetylcholinesterase inhibitors exceeded their respective TTC values. The modeled UCI exposures for the majority of the 1853 chemicals with genotoxicity structural alerts did exceed the TTC of 0.0025 μg/kg-day, but only 79 substances exceeded this TTC if median exposure values were used. For substances for which UCI exposures exceeded relevant TTC values, we highlight possible approaches for consideration to refine the HTE : TTC approach. Overall, coupling TTC with HTE offers promise as a pragmatic first step in ranking substances as part of a risk-based prioritization approach.

Phosphatidylethanol (PEth) is increasingly used as a biomarker of heavy drinking. Many different forms of PEth can form in red blood cell membranes from the action of the enzyme phospholipase D. PEth has a very long duration in blood because, in contrast to other tissues, RBCs lack the enzymes that degrade PEth. Because this biomarker is relatively new, interpretations of the analytical measurements of PEth may be misinterpreted and the resulting predictions of actual alcohol consumption inaccurate. Hence, a simple pharmacokinetic model of PEth was developed to provide a means of contextualizing these analytical results. A number of alcohol consumption scenarios and current clinical screening levels were examined with the model.

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.