Development and evaluation of a high throughput inhalation model for organic chemicals

Enabling transparent toxicokinetic modeling for public health risk assessment

Toxicokinetic modeling describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Chemical-specific in vivo toxicokinetic data is often unavailable for the thousands of chemicals in commerce. However, predictions from generalized toxicokinetic models allow for extrapolation from in vitro toxicological data, obtained via new approach methods (NAMs), to predict in vivo human health outcomes and provide key information on chemicals for public health risk assessment. The httk R package provides an open-source software tool containing a suite of generalized toxicokinetic models covering various exposure scenarios, a library of chemical-specific data from peer-reviewed high-throughput toxicokinetic (HTTK) studies, and other utility functions to parameterize and evaluate toxicokinetic models. Generalized HTTK models in httk use the open-source language MCSim to describe the compartmental and physiologically based toxicokinetics (PBTK). New HTTK models may be integrated into httk with a model description code file (C script generated via MCSim) and a model documentation file (R script). httk provides a series of functionalities such as model parameterization, in vivo-derived data for evaluating model predictions, unit conversion, Monte Carlo simulations for uncertainty propagation and biological variability, and other model utilities. Here, we describe in detail how to add new HTTK models into the httk package to leverage its pre-existing data and functionality. As a demonstration, we describe the integration of a gas inhalation PBTK model. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment that makes use of publicly available data on more than one thousand chemicals.

Reconstruction of exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol using computational fluid dynamics, physiologically based toxicokinetics and statistical modeling

OBJECTIVES

This study employed computational fluid dynamics (CFD), physiologically based toxicokinetics (PBTK), and statistical modeling to reconstruct exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol. By utilizing a validated CFD model, human respiratory deposition of MDI aerosol in different workload conditions was investigated, while a PBTK model was calibrated using experimental rat data. Biomonitoring data and Markov Chain Monte Carlo (MCMC) simulation were utilized for exposure assessment.

RESULTS

Deposition fraction of MDI in the respiratory tract at the light, moderate, and heavy activity were 0.038, 0.079, and 0.153, respectively. Converged MCMC results as the posterior means and prior values were obtained for several PBTK model parameters. In our study, we calibrated a rat model to investigate the transport, absorption, and elimination of 4,4'-MDI via inhalation exposure. The calibration process successfully captured experimental data in the lungs, liver, blood, and kidneys, allowing for a reasonable representation of MDI distribution within the rat model. Our calibrated model also represents MDI dynamics in the bloodstream, facilitating the assessment of bioavailability. For human exposure, we validated the model for recent and long-term MDI exposure using data from relevant studies.

CONCLUSION

Our computational models provide reasonable insights into MDI exposure, contributing to informed risk assessment and the development of effective exposure reduction strategies.

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

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

A regression analysis using simple descriptors for multiple dermal datasets: Going from individual membranes to the full skin

In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well-established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = -2.55 + 0.65logP - 0.0085MW, R2  = 0.91 (applicability domain for all datasets: MW ranges from 18 to >584 g/mol and -4 to >5 for logP). This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route; future modeling work will consider adding multiple compartments.

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

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

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

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

Evaluation of a rapid, generic human gestational dose model

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

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.

Case study on the impact of the source of metabolism parameters in next generation physiologically based pharmacokinetic models: Implications for occupational exposures to trimethylbenzenes

Physiologically based pharmacokinetic (PBPK) models are a means of making important linkages between exposure assessment and in vitro toxicity. A key constraint on rapid application of PBPK models in risk assessment is traditional reliance on substance-specific in vivo toxicokinetic data to evaluate model quality. Bounding conditions, in silico, in vitro, and chemical read-across approaches have been proposed as alternative sources for metabolic clearance estimates. A case study to test consistency of predictive ability across these approaches was conducted using trimethylbenzenes (TMB) as prototype chemicals. Substantial concordance was found among TMB isomers with respect to accuracy (or inaccuracy) of approaches to estimating metabolism; for example, the bounding conditions never reproduced the human in vivo toxicokinetic data within two-fold. Using only approaches that gave acceptable prediction of in vivo toxicokinetics for the source compound (1,2,4-TMB) substantially narrowed the range of plausible internal doses for a given external dose for occupational, emergency response, and environmental/community health risk assessment scenarios for TMB isomers. Thus, risk assessments developed using the target compound models with a constrained subset of metabolism estimates (determined for source chemical models) can be used with greater confidence that internal dosimetry will be estimated with accuracy sufficient for the purpose at hand.

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.

Exploring in vitro to in vivo extrapolation for exposure and health impacts of e-cigarette flavor mixtures

In vitro to in vivo extrapolation (IVIVE) leverages in vitro biological activities to predict corresponding in vivo exposures, therefore potentially reducing the need for animal safety testing that are traditionally performed to support the hazard and risk assessment. Interpretation of IVIVE predictions are affected by various factors including the model type, exposure route and kinetic assumptions for the test article, and choice of in vitro assay(s) that are relevant to clinical outcomes. Exposure scenarios are further complicated for mixtures where the in vitro activity may stem from one or more components in the mixture. In this study, we used electronic cigarette (EC) aerosols, a complex mixture, to explore impacts of these factors on the use of IVIVE in hazard identification, using open-source pharmacokinetic models of varying complexity and publicly available data. Results suggest in vitro assay selection has a greater impact on exposure estimates than modeling approaches. Using cytotoxicity assays, high exposure estimates (>1000 EC cartridges (pods) or > 700 mL EC liquid per day) would be needed to obtain the in vivo plasma levels that are corresponding to in vitro assay data, suggesting acute toxicity would be unlikely in typical usage scenarios. When mechanistic (Tox21) assays were used, the exposure estimates were much lower for the low end, but the range of exposure estimate became wider across modeling approaches. These proof-of-concept results highlight challenges and complexities in IVIVE for mixtures.