PBPK modeling to evaluate maximum tolerated doses: A case study with 3-chloroallyl alcohol.

Introduction: A physiologically based pharmacokinetic (PBPK) model for 3-chloroallyl alcohol (3-CAA) was developed and used to evaluate the design of assays for the in vivo genotoxicity of 3-CAA. Methods: Model development was supported by read across from a published PBPK model for ethanol. Read across was motivated by the expectation that 3-CAA, which like ethanol is a primary alcohol, is metabolized largely by hepatic alcohol dehydrogenases. The PBPK model was used to evaluate how two metrics of tissue dosimetry, maximum blood concentration (Cmax; mg/L) and area under the curve (AUC; mg-hr/L) vary with dose of 3-CAA and with dose route (oral gavage, drinking water). Results: The model predicted that oral gavage results in a 6-fold higher Cmax than the same dose administered in drinking water, but in similar AUCs. Predicted Cmax provided the best correlation with severe toxicity (e.g., lethality) from 3-CAA, consistent with the production of a reactive metabolite. Therefore, drinking water administration can achieve higher sustained concentration without severe toxicity in vivo. Discussion: This evaluation is significant because cytotoxicity is a potential confounder of mutagenicity testing. The PBPK model can be used to ensure that studies meet OECD and USEPA test guidelines and that the highest dose used is not associated with severe toxicity. In addition, PBPK modeling provides assurance of target tissue (e.g., bone marrow) exposure even in the absence of laboratory data, by defining the relationship between applied dose and target tissue dose based on accepted principles of pharmacokinetics, relevant physiology and biochemistry of the dosed animals, and chemical-specific information.

Mechanisms and shapes of causal exposure-response functions for asbestos in mesotheliomas and lung cancers.

This paper summarizes recent insights into causal biological mechanisms underlying the carcinogenicity of asbestos. It addresses their implications for the shapes of exposure-response curves and considers recent epidemiologic trends in malignant mesotheliomas (MMs) and lung fiber burden studies. Since the commercial amphiboles crocidolite and amosite pose the highest risk of MMs and contain high levels of iron, endogenous and exogenous pathways of iron injury and repair are discussed. Some practical implications of recent developments are that: (1) Asbestos-cancer exposure-response relationships should be expected to have non-zero background rates; (2) Evidence from inflammation biology and other sources suggests that there are exposure concentration thresholds below which exposures do not increase inflammasome-mediated inflammation or resulting inflammation-mediated cancer risks above background risk rates; and (3) The size of the suggested exposure concentration threshold depends on both the detailed time patterns of exposure on a time scale of hours to days and also on the composition of asbestos fibers in terms of their physiochemical properties. These conclusions are supported by complementary strands of evidence including biomathematical modeling, cell biology and biochemistry of asbestos-cell interactions in vitro and in vivo, lung fiber burden analyses and epidemiology showing trends in human exposures and MM rates.

Updating the biologically based dose-response model for the nasal carcinogenicity of inhaled formaldehyde in the F344 rat.

Chronic inhalation of formaldehyde by F344 rats causes nasal squamous cell carcinoma (SCC). This outcome is well-characterized: including dose-response and time course data for SCC, mechanistic endpoints, and nasal dosimetry. Conolly et al. (Toxicol. Sci. 75, 432-447, 2003) used these resources to develop a biologically based dose-response (BBDR) model for SCC in F344 rats. This model, scaled up to humans, has informed dose-response conclusions reached by several international regulatory agencies. However, USEPA concluded that uncertainties precluded its use for cancer risk assessment. Here, we describe an updated BBDR model that addresses uncertainties through refined dosimetry modeling, revised analysis of labeling index data, and an extended dataset where both inhaled (exogenous) and endogenous formaldehyde (exogF, endoF) form DNA adducts. Further, since Conolly et al. (ibid) was published, it has become clear that, when controls from all F344 inhalation bioassays are considered, accounting for over 4000 rats, at most one nasal SCC occurred. This low spontaneous incidence constrains possible contribution of endoF to the formation of nasal SCC via DNA reactivity. Further, since both exogF and endoF form DNA adducts, this constraint also applies to exogF. The revised BBDR model therefore drives SCC formation through the cytotoxicity of high concentration exogF. An option for direct mutagenicity associated with DNA adducts is retained to allow estimation of an upper bound on adduct mutagenicity consistent with the lack of a spontaneous SCC incidence. These updates represent an iterative refinement of the 2003 model, incorporating new data and insights to reduce identified model uncertainties.

Relative contributions of endogenous and exogenous formaldehyde to formation of deoxyguanosine monoadducts and DNA-protein crosslink adducts of DNA in rat nasal mucosa.

Understanding the dose-response for formaldehyde-induced nasal cancer in rats is complicated by (1) the uneven distribution of inhaled formaldehyde across the interior surface of the nasal cavity and, (2) the presence of endogenous formaldehyde (endoF) in the nasal mucosa. In this work, we used computational fluid dynamics (CFD) modeling to predict flux of inhaled (exogenous) formaldehyde (exogF) from air into tissue at the specific locations where DNA adducts were measured. Experimental work has identified DNA-protein crosslink (DPX) adducts due to exogF and deoxyguanosine (DG) adducts due to both exogF and endoF. These adducts can be considered biomarkers of exposure for effects of endoF and exogF on DNA that may be part of the mechanism of tumor formation. We describe a computational model linking CFD-predicted flux of formaldehyde from air into tissue, and the intracellular production of endoF, with the formation of DPX and DG adducts. We assumed that, like exogF, endoF can produce DPX. The model accurately reproduces exogDPX, exogDG, and endoDG data after inhalation from 0.7 to 15 ppm. The dose-dependent concentrations of exogDPX and exogDG are predicted to exceed the concentrations of their endogenous counterparts at about 2 and 6 ppm exogF, respectively. At all concentrations examined, the concentrations of endoDPX and exogDPX were predicted to be at least 10-fold higher than that of their DG counterparts. The modeled dose-dependent concentrations of these adducts are suitable to be used together with data on the dose-dependence of cell proliferation to conduct quantitative modeling of formaldehyde-induced rat nasal carcinogenicity.

Case Study in 21st-Century Ecotoxicology: Using In Vitro Aromatase Inhibition Data to Predict Reproductive Outcomes in Fish In Vivo.

To reduce the use of intact animals for chemical safety testing, while ensuring protection of ecosystems and human health, there is a demand for new approach methodologies (NAMs) that provide relevant scientific information at a quality equivalent to or better than traditional approaches. The present case study examined whether bioactivity and associated potency measured in an in vitro screening assay for aromatase inhibition could be used together with an adverse outcome pathway (AOP) and mechanistically based computational models to predict previously uncharacterized in vivo effects. Model simulations were used to inform designs of 60-h and 10-21-day in vivo exposures of adult fathead minnows (Pimephales promelas) to three or four test concentrations of the in vitro aromatase inhibitor imazalil ranging from 0.12 to 260 µg/L water. Consistent with an AOP linking aromatase inhibition to reproductive impairment in fish, exposure to the fungicide resulted in significant reductions in ex vivo production of 17ß-estradiol (E2) by ovary tissue (≥165 µg imazalil/L), plasma E2 concentrations (≥74 µg imazalil/L), vitellogenin (Vtg) messenger RNA expression (≥165 µg imazalil/L), Vtg plasma concentrations (≥74 µg imazalil/L), uptake of Vtg into oocytes (≥260 µg imazalil/L), and overall reproductive output in terms of cumulative fecundity, number of spawning events, and eggs per spawning event (≥24 µg imazalil/L). Despite many potential sources of uncertainty in potency and efficacy estimates based on model simulations, observed magnitudes of apical effects were quite consistent with model predictions, and in vivo potency was within an order of magnitude of that predicted based on in vitro relative potency. Overall, our study suggests that NAMs and AOP-based approaches can support meaningful reduction and refinement of animal testing. Environ Toxicol Chem 2023;42:100-116. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A Physiologically Based Pharmacokinetic (PBPK) Modeling Framework for Mixtures of Dioxin-like Compounds.

Humans are exposed to persistent organic pollutants, such as dioxin-like compounds (DLCs), as mixtures. Understanding and predicting the toxicokinetics and thus internal burden of major constituents of a DLC mixture is important for assessing their contributions to health risks. PBPK models, including dioxin models, traditionally focus on one or a small number of compounds; developing new or extending existing models for mixtures often requires tedious, error-prone coding work. This lack of efficiency to scale up for multi-compound exposures is a major technical barrier toward large-scale mixture PBPK simulations. Congeners in the DLC family, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), share similar albeit quantitatively different toxicokinetic and toxicodynamic properties. Taking advantage of these similarities, here we reported the development of a human PBPK modeling framework for DLC mixtures that can flexibly accommodate an arbitrary number of congeners. Adapted from existing TCDD models, our mixture model contains the blood and three diffusion-limited compartments-liver, fat, and rest of the body. Depending on the number of congeners in a mixture, varying-length vectors of ordinary differential equations (ODEs) are automatically generated to track the tissue concentrations of the congeners. Shared ODEs are used to account for common variables, including the aryl hydrocarbon receptor (AHR) and CYP1A2, to which the congeners compete for binding. Binary and multi-congener mixture simulations showed that the AHR-mediated cross-induction of CYP1A2 accelerates the sequestration and metabolism of DLC congeners, resulting in consistently lower tissue burdens than in single exposure, except for the liver. Using dietary intake data to simulate lifetime exposures to DLC mixtures, the model demonstrated that the relative contributions of individual congeners to blood or tissue toxic equivalency (TEQ) values are markedly different than those to intake TEQ. In summary, we developed a mixture PBPK modeling framework for DLCs that may be utilized upon further improvement as a quantitative tool to estimate tissue dosimetry and health risks of DLC mixtures.

Pregnancy-specific physiologically-based toxicokinetic models for bisphenol A and bisphenol S.

Predictions from physiologically based toxicokinetic (PBTK) models can help inform human health risk assessment for potentially toxic chemicals in the environment. Bisphenol S (BPS) is the second most abundant bisphenol detected in humans in the United States, after bisphenol A (BPA). We have recently demonstrated that BPS, much like BPA, can cross the placental barrier and disrupt placental function. Differences in physicochemical properties, toxicokinetics, and exposure outcomes between BPA and other bisphenols prevent direct extrapolation of existing BPA PBTK models to BPS. The current study aimed to develop pregnancy-specific PBTK (p-PBTK) models for BPA and BPS, using a common p-PBTK model structure. Novel paired maternal and fetal pregnancy data sets for total, unconjugated, and conjugated BPA and BPS plasma concentrations from three independent studies in pregnant sheep were used for model calibration. The nine-compartment (maternal blood, liver, kidney, fat, placenta and rest of body, and fetal liver, blood and rest of body) models simulated maternal and fetal experimental data for both BPA and BPS within one standard deviation for the majority of the experimental data points, highlighting the robustness of both models. Simulations were run to examine fetal exposure following daily maternal exposure to BPA or BPS at their tolerable daily intake dose over a two-week period. These predictive simulations show fetal accumulation of both bisphenols over time. Interestingly, the steady-state approximation following this dosing strategy achieved a fetal concentration of unconjugated BPA to levels observed in cord blood from human biomonitoring studies. These models advance our understanding of bisphenolic compound toxicokinetics during pregnancy and may be used as a quantitative comparison tool in future p-PBTK models for related chemicals.

Embracing Systems Toxicology at Single-Cell Resolution.

As systems biology expands its multi-omic spectrum to increasing resolutions, distinguishing cells based on single-cell profiles becomes feasible. Unlike traditional bulk assays that average cellular responses and blur the distinct identities of responsive cells, single-cell technologies enable sensitive detection of small cellular changes and precise identification of those cells perturbed by toxicants. Among the suite of omic technologies that continue to expand and become affordable, single-cell RNA sequencing (scRNA-seq) is at the cutting edge and leading the way to transform systems toxicology. Single-cell systems toxicology can provide a wealth of information to elucidate cell-specific alterations and response trajectories, detect points-of-departure, map and develop dynamical models of toxicity pathways.

Development of a Novel AOP for Cyp2F2-Mediated Lung Cancer in Mice.

Traditional methods for carcinogenicity testing rely heavily on the rodent bioassay as the standard for identification of tumorigenic risk. As such, identification of species-specific outcomes and/or metabolism are a frequent argument for regulatory exemption. One example is the association of tumor formation in the mouse lung after exposure to Cyp2F2 ligands. The adverse outcome pathway (AOP) framework offers a theoretical platform to address issues of species specificity that is consistent, transparent, and capable of integrating data from new approach methodologies as well as traditional data streams. A central premise of the AOP concept is that pathway progression from the molecular initiating event (MIE) implies a definable "response-response" (R-R) relationship between each key event (KE) that drives the pathway towards a specific adverse outcome (AO). This article describes an AOP for lung cancer in the mouse from an MIE of Cyp2F2-specific reactive metabolite formation, advancing through KE that include protein and/or nucleic acid adducts, diminished Club Cell 10 kDa (CC10) protein expression, hyperplasia of CC10 deficient Club cells, and culminating in the AO of mixed-cell tumor formation in the distal airways. This tumor formation is independent of route of exposure and our AOP construct is based on overlapping mechanistic events for naphthalene, styrene, ethyl benzene, isoniazid, and fluensulfone in the mouse. This AOP is intended to accelerate the explication of an apparent mouse-specific outcome and serve as a starting point for a quantitative analysis of mouse-human differences in susceptibility to the tumorigenic effects of Cyp2F2 ligands.

A Quantitative Source-to-Outcome Case Study To Demonstrate the Integration of Human Health and Ecological End Points Using the Aggregate Exposure Pathway and Adverse Outcome Pathway Frameworks.

Exposure to environmental contaminants can lead to adverse outcomes in both human and nonhuman receptors. The Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks can mechanistically inform cumulative risk assessment for human health and ecological end points by linking together environmental transport and transformation, external exposure, toxicokinetics, and toxicodynamics. This work presents a case study of a hypothetical contaminated site to demonstrate a quantitative approach for implementing the AEP framework and linking this framework to AOPs. We construct an AEP transport and transformation model and then quantify external exposure pathways for humans, fishes, and small herbivorous mammals at the hypothetical site. A Monte Carlo approach was used to address parameter variability. Source apportionment was quantified for each species, and published pharmacokinetic models were used to estimate internal target site exposure from external exposures. Published dose-response data for a multispecies AOP network were used to interpret AEP results in the context of species-specific effects. This work demonstrates (1) the construction, analysis, and application of a quantitative AEP model, (2) the utility of AEPs for organizing mechanistic exposure data and highlighting data gaps, and (3) the advantages provided by a source-to-outcome construct for leveraging exposure data and to aid transparency regarding assumptions.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment.

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

High-Throughput Video Processing of Heart Rate Responses in Multiple Wild-type Embryonic Zebrafish per Imaging Field.

Heart rate assays in wild-type zebrafish embryos have been limited to analysis of one embryo per video/imaging field. Here we present for the first time a platform for high-throughput derivation of heart rate from multiple zebrafish (Danio rerio) embryos per imaging field, which is capable of quickly processing thousands of videos and ideal for multi-well platforms with multiple fish/well. This approach relies on use of 2-day post fertilization wild-type embryos, and uses only bright-field imaging, circumventing requirement for anesthesia or restraint, costly software/hardware, or fluorescently-labeled animals. Our original scripts (1) locate the heart and record pixel intensity fluctuations generated by each cardiac cycle using a robust image processing routine, and (2) process intensity data to derive heart rate. To demonstrate assay utility, we exposed embryos to the drugs epinephrine and clonidine, which increased or decreased heart rate, respectively. Exposure to organic extracts of air pollution-derived particulate matter, including diesel or biodiesel exhausts, or wood smoke, all complex environmental mixtures, decreased heart rate to varying degrees. Comparison against an established lower-throughput method indicated robust assay fidelity. As all code and executable files are publicly available, this approach may expedite cardiotoxicity screening of compounds as diverse as small molecule drugs and complex chemical mixtures.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling.

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

Changes in Metabolites Present in Lung-Lining Fluid Following Exposure of Humans to Ozone.

Controlled human exposure to the oxidant air pollutant ozone causes decrements in lung function and increased inflammation as evidenced by neutrophil influx into the lung and increased levels of proinflammatory cytokines in the airways. Here we describe a targeted metabolomics evaluation of human bronchoalveolar lavage fluid (BALF) following controlled in vivo exposure to ozone to gain greater insight into its pulmonary effects. In a 2-arm cross-over study, each healthy adult human volunteer was randomly exposed to filtered air (FA) and to 0.3 ppm ozone for 2 h while undergoing intermittent exercise with a minimum of 4 weeks between exposures. Bronchoscopy was performed and BALF obtained at 1 (n = 9) or 24 (n = 23) h postexposure. Metabolites were detected using ultrahigh performance liquid chromatography-tandem mass spectroscopy. At 1-h postexposure, a total of 28 metabolites were differentially expressed (DE) (p < .05) following ozone exposure compared with FA-exposure. These changes were associated with increased glycolysis and antioxidant responses, suggesting rapid increased energy utilization as part of the cellular response to oxidative stress. At 24-h postexposure, 41 metabolites were DE. Many of the changes were in amino acids and linked with enhanced proteolysis. Changes associated with increased lipid membrane turnover were also observed. These later-stage changes were consistent with ongoing repair of airway tissues. There were 1.37 times as many metabolites were differentially expressed at 24 h compared with 1-h postexposure. The changes at 1 h reflect responses to oxidative stress while the changes at 24 h indicate a broader set of responses consistent with tissue repair. These results illustrate the ability of metabolomic analysis to identify mechanistic features of ozone toxicity and aspects of the subsequent tissue response.

A Case Study Application of the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) Frameworks to Facilitate the Integration of Human Health and Ecological End Points for Cumulative Risk Assessment (CRA).

Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from multiple stressors. While CSMs may encompass multiple species, evaluating end points across taxa can be challenging due to data availability and physiological differences among organisms. Adverse outcome pathways (AOPs) describe biological mechanisms leading to adverse outcomes (AOs) by assembling causal pathways with measurable intermediate steps termed key events (KEs), thereby providing a framework for integrating data across species. In this work, we used a case study focused on the perchlorate anion (ClO4-) to highlight the value of the AOP framework for cross-species data integration. Computational models and dose-response data were used to evaluate the effects of ClO4- in 12 species and revealed a dose-response concordance across KEs and taxa. The aggregate exposure pathway (AEP) tracks stressors from sources to the exposures and serves as a complement to the AOP. We discuss how the combined AEP-AOP construct helps to maximize the use of existing data and advances CRA by (1) organizing toxicity and exposure data, (2) providing a mechanistic framework of KEs for integrating data across human health and ecological end points, (3) facilitating cross-species dose-response evaluation, and (4) highlighting data gaps and technical limitations.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology.

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response, and time-course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for the following: (a) the hypothalamic-pitutitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17ß-estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yolk protein precursor) synthesis, (b) VTG-dependent egg development and spawning (fecundity), and (c) fecundity-dependent population trajectory. While development of the example qAOP was based on experiments with FHMs exposed to the aromatase inhibitor fadrozole, we also show how a toxic equivalence (TEQ) calculation allows use of the qAOP to predict effects of another, untested aromatase inhibitor, iprodione. While qAOP development can be resource-intensive, the quantitative predictions obtained, and TEQ-based application to multiple chemicals, may be sufficient to justify the cost for some applications in regulatory decision-making.

An updated analysis of respiratory tract cells at risk for formaldehyde carcinogenesis.

Study of the mode of action (MOA) relating exposure to a given chemical with an associated adverse outcome is an iterative process with each iteration driven by new understandings of the relevant biology. Here, we revisit a previously described, MOA-based clonal growth model of the human respiratory tract cancer risk associated with formaldehyde inhalation. Changes reflect a better understanding of populations of cells at risk of carcinogenic transformation in the pharynx, larynx and respiratory bronchiolar portions of the human respiratory tract and inclusion of basal cells in the pool of cells at risk. The focus of this report is not on cancer risk per se, but rather on the sensitivity of model parameters and predicted risks to alternative descriptions of the fraction of cells at risk for carcinogenic transformation. For a population of formaldehyde-exposed nonsmokers, revised specification of cells at risk resulted in changes in both parameter estimates and in predicted risks. Compared to our previous assessment, predicted additional risks were up to 87% greater at exposure levels ≤1 ppm, but up to about 130% lower at high exposure levels (2-5 ppm). While this work should not be considered an update to MOA-based risk assessments for formaldehyde described previously, it illustrates the sensitivity of parameter estimates and risk predictions to the quantitative specification of cells at risk of carcinogenic transformation and, therefore, the motivation for describing the relevant biology as accurately as possible.

Editor's Highlight: Computational Modeling of Plasma Vitellogenin Alterations in Response to Aromatase Inhibition in Fathead Minnows.

In vertebrates, conversion of testosterone into 17ß-estradiol (E2) is catalyzed by cytochrome P450 (CYP) 19A aromatase. An important role of E2 in oviparous vertebrates such as fish is stimulation of hepatic synthesis of the glycolipoprotein vitellogenin (VTG), an egg yolk precursor essential to oocyte development and larval survival. In fathead minnows (FHMs) (Pimephales promelas) exposed to the aromatase inhibitor fadrozole, plasma VTG levels do not change in concert with plasma E2 levels. Specifically, while plasma VTG and E2 levels both drop quickly when aromatase is first inhibited, the recovery of plasma VTG upon cessation of aromatase inhibition is substantially delayed relative to the recovery of plasma E2. We modified an existing computational model of the FHM hypothalamic-pituitary-gonadal axis to evaluate alternative hypotheses that might explain this delay. In the first hypothesis, a feedback loop involving active transport of VTG from the blood into the ovary is used. The activity of the transporter is negatively regulated by ovarian VTG. In the second hypothesis, a type 1 coherent feed-forward loop is implemented in the liver. This loop has 2 arms, both requiring E2 activation. The first arm describes direct, canonical E2-driven transcriptional induction of VTG, and the second describes an E2-driven intermediate transcriptional regulator that is also required for VTG synthesis. Both hypotheses accurately described the observed VTG dynamics. This result could be used to guide design of laboratory experiments intended to determine if either of the motifs, or perhaps even both of them, actually do control VTG dynamics in FHMs exposed to aromatase inhibitors.

Developing a Gene Biomarker at the Tipping Point of Adaptive and Adverse Responses in Human Bronchial Epithelial Cells.

Determining mechanism-based biomarkers that distinguish adaptive and adverse cellular processes is critical to understanding the health effects of environmental exposures. Shifting from in vivo, low-throughput toxicity studies to high-throughput screening (HTS) paradigms and risk assessment based on in vitro and in silico testing requires utilizing toxicity pathway information to distinguish adverse outcomes from recoverable adaptive events. Little work has focused on oxidative stresses in human airway for the purposes of predicting adverse responses. We hypothesize that early gene expression-mediated molecular changes could be used to delineate adaptive and adverse responses to environmentally-based perturbations. Here, we examined cellular responses of the tracheobronchial airway to zinc (Zn) exposure, a model oxidant. Airway derived BEAS-2B cells exposed to 2-10 µM Zn2+ elicited concentration- and time-dependent cytotoxicity. Normal, adaptive, and cytotoxic Zn2+ exposure conditions were determined with traditional apical endpoints, and differences in global gene expression around the tipping point of the responses were used to delineate underlying molecular mechanisms. Bioinformatic analyses of differentially expressed genes indicate early enrichment of stress signaling pathways, including those mediated by the transcription factors p53 and NRF2. After 4 h, 154 genes were differentially expressed (p < 0.01) between the adaptive and cytotoxic Zn2+ concentrations. Nearly 40% of the biomarker genes were related to the p53 signaling pathway with 30 genes identified as likely direct targets using a database of p53 ChIP-seq studies. Despite similar p53 activation profiles, these data revealed widespread dampening of p53 and NRF2-related genes as early as 4 h after exposure at higher, unrecoverable Zn2+ exposures. Thus, in our model early increased activation of stress response pathways indicated a recoverable adaptive event. Overall, this study highlights the importance of characterizing molecular mechanisms around the tipping point of adverse responses to better inform HTS paradigms.