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.

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.

All-or-none suppression of B cell terminal differentiation by environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin.

Many environmental contaminants can disrupt the adaptive immune response. Exposure to the ubiquitous aryl hydrocarbon receptor (AhR) ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and other agonists suppresses the antibody response. The underlying pathway mechanism by which TCDD alters B cell function is not well understood. The present study investigated the mechanism of AhR-mediated pathways and mode of suppression by which TCDD perturbs terminal differentiation of B cells to plasma cells and thereby impairs antibody production. An integrated approach combining computational pathway modeling and in vitro assays with primary mouse B cells activated by lipopolysaccharide was employed. We demonstrated that suppression of the IgM response by TCDD occurs in an all-or-none (binary) rather than graded mode: i.e., it reduces the number of IgM-secreting cells in a concentration-dependent manner without affecting the IgM content in individual plasma cells. The mathematical model of the gene regulatory circuit underpinning B cell differentiation revealed that two previously identified AhR-regulated pathways, inhibition of signaling protein AP-1 and activation of transcription factor Bach2, could account for the all-or-none mode of suppression. Both pathways disrupt the operation of a bistable-switch circuit that contains transcription factors Bcl6, Prdm1, Pax5, and Bach2 and regulates B cell fate. The model further predicted that by transcriptionally activating Bach2, TCDD might delay B cell differentiation and increase the likelihood of isotype switching, thereby altering the antibody repertoire. In conclusion, the present study revealed the mode and specific pathway mechanisms by which the environmental immunosuppressant TCDD suppresses B cell differentiation.

A framework for fit-for-purpose dose response assessment.

The NRC report Science and Decisions: Advancing Risk Assessment made several recommendations to improve chemical risk assessment, with a focus on in-depth chronic dose-response assessments conducted by the U.S. Environmental Protection Agency. The recommendations addressed two broad elements: improving technical analysis and utility for decision making. To advance the discussions in the NRC report, in three multi-stakeholder workshops organized by the Alliance for Risk Assessment, available and evolving risk assessment methodologies were considered through the development and application of case studies. A key product was a framework (http://www.allianceforrisk.org/Workshop/Framework/ProblemFormulation.html) to guide risk assessors and managers to various dose-response assessment methods relevant to a range of decision contexts ranging from priority setting to full assessment, as illustrated by case studies. It is designed to facilitate selection of appropriate methodology for a variety of problem formulations and includes a variety of methods with supporting case studies, for areas flagged specifically by the NRC committee for consideration--e.g., susceptible sub-populations, population variability and background. The framewok contributes to organization and communication about methodologies for incorporating increasingly biologically informed and chemical specific knowledge into dose-response analysis, which is considered critical in evolving fit-for-purpose assessment to address relevant problem formulations.

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.

A proposed framework for assessing risk from less-than-lifetime exposures to carcinogens.

Quantitative methods for estimation of cancer risk have been developed for daily, lifetime human exposures. There are a variety of studies or methodologies available to address less-than-lifetime exposures. However, a common framework for evaluating risk from less-than-lifetime exposures (including short-term and/or intermittent exposures) does not exist, which could result in inconsistencies in risk assessment practice. To address this risk assessment need, a committee of the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute conducted a multisector workshop in late 2009 to discuss available literature, different methodologies, and a proposed framework. The proposed framework provides a decision tree and guidance for cancer risk assessments for less-than-lifetime exposures based on current knowledge of mode of action and dose-response. Available data from rodent studies and epidemiological studies involving less-than-lifetime exposures are considered, in addition to statistical approaches described in the literature for evaluating the impact of changing the dose rate and exposure duration for exposure to carcinogens. The decision tree also provides for scenarios in which an assumption of potential carcinogenicity is appropriate (e.g., based on structural alerts or genotoxicity data), but bioassay or other data are lacking from which a chemical-specific cancer potency can be determined. This paper presents an overview of the rationale for the workshop, reviews historical background, describes the proposed framework for assessing less-than-lifetime exposures to potential human carcinogens, and suggests next steps.

The fractions of respiratory tract cells at risk in formaldehyde carcinogenesis.

Clonal growth modeling of carcinogenesis requires data on the number of cells at risk of becoming cancerous. We synthesized literature data to estimate the fraction of respiratory tract epithelial cells that are progenitor cells, and therefore at risk, in formaldehyde carcinogenesis for specific respiratory tract regions. We concluded that the progenitor cells for the transitional and respiratory epithelia of the nose are basal and nonciliated cells and Type II cells in the alveolar region. In the conducting airways, our evaluation indicated that ciliated and basal cells are not in the progenitor pool. Respiratory tract epithelial cell fractions of 0.819 in rats and 0.668 in humans were estimated from the data. The total numbers of epithelial cells in the lower respiratory tract of humans and rats were allocated to individual generations. Cell cycle times were also estimated from literature data, since the reciprocal of cell cycle time is an important variable in clonal growth modeling. Sensitivity analyses of a previously published risk model for formaldehyde carcinogenesis showed that specification of the fraction of cells at risk markedly affects estimates of some parameters of the clonal growth model. When all epithelial cells are considered part of the progenitor pool, additional risks for the non-smoking population was typically over predicted by about 35% for high exposure levels. These results demonstrate the importance of accurately identifying cell populations at risk when applying quantitative models in risk assessments.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

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.

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.

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.

How can biologically-based modeling of arsenic kinetics and dynamics inform the risk assessment process? - A workshop review.

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic metabolizing enzymes can lead to differences in target tissue dosimetry for key metabolites causative in toxic and carcinogenic response. This type of variation can be quantitatively incorporated into pharmacokinetic (PK) models and used together with population-based modeling approaches to evaluate the impact of genetic variation in methylation capacity on dose of key metabolites to target tissue. The PK model is an essential bridge to the pharmacodynamic (PD) models. A particular benefit of PD modeling for arsenic is that alternative models can be constructed for multiple proposed modes of action for arsenicals. Genomics data will prove useful for identifying the key pathways involved in particular responses and aid in determining other types of data needed for quantitative modeling. These models, when linked with PK models, can be used to better understand and explain dose- and time-response behaviors. This in turn assists in prioritizing modes of action with respect to their risk assessment relevance and future research. This type of integrated modeling approach can form the basis for a highly informative mode-of-action directed risk assessment for inorganic arsenic (iAs). This paper will address both practical and theoretical aspects of integrating PK and PD data in a modeling framework, including practical barriers to its application.

Assessing the health effects and risks associated with children's inhalation exposures--asthma and allergy.

Adults and children may have different reactions to inhalation exposures due to differences in target tissue doses following similar exposures, and/or different stages in lung growth and development. In the case of asthma and allergy both the developing immune system and initial encounters with common allergens contribute to this differential susceptibility. Asthma, the most common chronic childhood disease, has significant public health impacts and is characterized by chronic lung inflammation, reversible airflow obstruction, and immune sensitization to allergens. Animal studies described here suggest that air pollutants exacerbate asthma symptoms and may also play a role in disease induction. Changes characteristic of asthma were observed in rhesus monkeys sensitized to house dust mite antigen (HDMA) as infants and exposed repeatedly thereafter to ozone (O3) and HDMA. O3 exposure compromised airway growth and development and exacerbated the allergen response to favor intermittent airway obstruction and wheeze. In Brown Norway rats a variety of air pollutants enhanced sensitization to HDMA such that symptoms elicited in response to subsequent allergen challenge were more severe. Although useful for assessing air pollutants effects on initial sensitization, the rodent immune system is immature at birth relative to humans, making this model less useful for studying differential effects between adults and children. Because computational models available to address children's inhalation exposures are limited, default adjustments and their associated uncertainty will continue to be used in children's inhalation risk assessment. Because asthma is a complex (multiple genes, phenotypes, organ systems) disease, this area is ripe for systems biology approaches.

Bayesian calibration of a physiologically based pharmacokinetic/pharmacodynamic model of carbaryl cholinesterase inhibition.

Carbaryl, an N-methyl carbamate (NMC), is a common insecticide that reversibly inhibits neuronal cholinesterase activity. The objective of this work was to use a hierarchical Bayesian approach to estimate the parameters in a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model from experimental measurements of carbaryl in rats. A PBPK/PD model was developed to describe the tissue dosimetry of carbaryl and its metabolites (1-naphthol and "other hydroxylated metabolites") and subsequently to predict the carbaryl-induced inhibition of cholinesterase activity, in particular in the brain and blood. In support of the model parameterization, kinetic tracer studies were undertaken to determine total radioactive tissue levels of carbaryl and metabolites in rats exposed by oral or intravenous routes at doses ranging from 0.8 to 9.2 mg/kg body weight. Inhibition of cholinesterase activity in blood and brain was also measured from the exposed rats. Markov Chain Monte Carlo (MCMC) calibration of the rat model parameters was implemented using prior information from literature for physiological parameter distributions together with kinetic and inhibition data on carbaryl. The posterior estimates of the parameters displayed at most a twofold deviation from the mean. Monte Carlo simulations of the PBPK/PD model with the posterior distribution estimates predicted a 95% credible interval of tissue doses for carbaryl and 1-naphthol within the range of observed data. Similar prediction results were achieved for cholinesterase inhibition by carbaryl. This initial model will be used to determine the experimental studies that may provide the highest added value for model refinement. The Bayesian PBPK/PD modeling approach developed here will serve as a prototype for developing mechanism-based risk models for the other NMCs.

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.