PBPK model reporting template for chemical risk assessment applications.

Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals. This harmonized reporting template can be adopted and customized by public health agencies receiving PBPK model submission, and it can also serve as general guidance for submitting PBPK-related studies for publication in journals or other modeling sharing purposes. The current effort represents one of several ongoing collaborations among the PBPK modeling and risk assessment communities to promote, when appropriate, incorporating PBPK modeling to characterize the influence of pharmacokinetics on safety decisions made by regulatory agencies.

An evaluation of the USEPA Proposed Approaches for applying a biologically based dose-response model in a risk assessment for perchlorate in drinking water.

The United States Environmental Protection Agency's (USEPA) 2017 report, "Draft Report: Proposed Approaches to Inform the Derivation of a Maximum Contaminant Level Goal for Perchlorate in Drinking Water", proposes novel approaches for deriving a Maximum Contaminant Level Goal (MCLG) for perchlorate using a biologically-based dose-response (BBDR) model. The USEPA (2017) BBDR model extends previously peer-reviewed perchlorate models to describe the relationship between perchlorate exposure and thyroid hormone levels during early pregnancy. Our evaluation focuses on two key elements of the USEPA (2017) report: the plausibility of BBDR model revisions to describe control of thyroid hormone production in early pregnancy and the basis for linking BBDR model results to neurodevelopmental outcomes. While the USEPA (2017) BBDR model represents a valuable research tool, the lack of supporting data for many of the model assumptions and parameters calls into question the fitness of the extended BBDR model to support quantitative analyses for regulatory decisions on perchlorate in drinking water. Until more data can be developed to address uncertainties in the current BBDR model, USEPA should continue to rely on the RfD recommended by the NAS (USEPA, 2005) when considering further regulatory action.

Lipid, water, and protein composition to facilitate kinetic modeling of the auditory pathway.

Environments combining JP-8 jet fuel exposure with heightened ambient noise may accelerate hearing loss induced by noise. To reduce animal use and facilitate kinetic modeling of this military aviation fuel, tissue-specific parameters are required, including water, protein, and lipid content. However, tissues involved in hearing, including cochlea, brainstem, frontal, and temporal lobe, have not been characterized before. Therefore, water content was determined by lyophilization of rat auditory tissues and the protein of the freeze dried remainder was quantified using a bicinchoninic acid assay. Lipids were extracted from fresh-frozen rat auditory tissues and separated into neutral lipids, free fatty acids, neutral phospholipids, and acidic phospholipids using solid phase extraction. Phospholipid fractions were confirmed by 31 P nuclear magnetic resonance analysis showing distinct phospholipid profiles. Lipid content in reference tissues, such as kidney and adipose, confirmed literature values. For the first time, lipid content in the rat auditory pathway was determined showing that total lipid content was lowest in cochlea and highest in brainstem compared with frontal and temporal lobes. Auditory tissues displayed distinct lipid fraction profiles. The information on water, protein, and lipid composition is necessary to validate algorithms used in mathematical models and predict partitioning of chemicals of future interest into these tissues. This research may reduce the use of animals to measure partition coefficients for prospective physiological models.

Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase.

Organophosphates are a group of pesticides and chemical warfare nerve agents that inhibit acetylcholinesterase, the enzyme responsible for hydrolysis of the excitatory neurotransmitter acetylcholine. Numerous structural variants exist for this chemical class, and data regarding their toxicity can be difficult to obtain in a timely fashion. At the same time, their use as pesticides and military weapons is widespread, which presents a major concern and challenge in evaluating human toxicity. To address this concern, a quantitative structure-activity relationship (QSAR) was developed to predict pentavalent organophosphate oxon human acetylcholinesterase bimolecular rate constants. A database of 278 three-dimensional structures and their bimolecular rates was developed from 15 peer-reviewed publications. A database of simplified molecular input line entry notations and their respective acetylcholinesterase bimolecular rate constants are listed in Supplementary Material, Table I. The database was quite diverse, spanning 7 log units of activity. In order to describe their structure, 675 molecular descriptors were calculated using AMPAC 8.0 and CODESSA 2.7.10. Orthogonal projection to latent structures regression, bootstrap leave-random-many-out cross-validation and y-randomization were used to develop an externally validated consensus QSAR model. The domain of applicability was assessed by the William's plot. Six external compounds were outside the warning leverage indicating potential model extrapolation. A number of compounds had residuals >2 or <-2, indicating potential outliers or activity cliffs. The results show that the HOMO-LUMO energy gap contributed most significantly to the binding affinity. A mean training R (2) of 0.80, a mean test set R (2) of 0.76 and a consensus external test set R (2) of 0.66 were achieved using the QSAR. The training and external test set RMSE values were found to be 0.76 and 0.88. The results suggest that this QSAR model can be used in physiologically based pharmacokinetic/pharmacodynamic models of organophosphate toxicity to determine the rate of acetylcholinesterase inhibition.

Quantitative structure-activity relationships for organophosphates binding to trypsin and chymotrypsin.

Organophosphate (OP) nerve agents such as sarin, soman, tabun, and O-ethyl S-[2-(diisopropylamino) ethyl] methylphosphonothioate (VX) do not react solely with acetylcholinesterase (AChE). Evidence suggests that cholinergic-independent pathways over a wide range are also targeted, including serine proteases. These proteases comprise nearly one-third of all known proteases and play major roles in synaptic plasticity, learning, memory, neuroprotection, wound healing, cell signaling, inflammation, blood coagulation, and protein processing. Inhibition of these proteases by OP was found to exert a wide range of noncholinergic effects depending on the type of OP, the dose, and the duration of exposure. Consequently, in order to understand these differences, in silico biologically based dose-response and quantitative structure-activity relationship (QSAR) methodologies need to be integrated. Here, QSAR were used to predict OP bimolecular rate constants for trypsin and α-chymotrypsin. A heuristic regression of over 500 topological/constitutional, geometric, thermodynamic, electrostatic, and quantum mechanical descriptors, using the software Ampac 8.0 and Codessa 2.51 (SemiChem, Inc., Shawnee, KS), was developed to obtain statistically verified equations for the models. General models, using all data subsets, resulted in R(2) values of .94 and .92 and leave-one-out Q(2) values of 0.9 and 0.87 for trypsin and α-chymotrypsin. To validate the general model, training sets were split into independent subsets for test set evaluation. A y-randomization procedure, used to estimate chance correlation, was performed 10,000 times, resulting in mean R(2) values of .24 and .3 for trypsin and α-chymotrypsin. The results show that these models are highly predictive and capable of delineating the complex mechanism of action between OP and serine proteases, and ultimately, by applying this approach to other OP enzyme reactions such as AChE, facilitate the development of biologically based dose-response models.

A Bayesian network model for biomarker-based dose response.

A Bayesian network model was developed to integrate diverse types of data to conduct an exposure-dose-response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum. The network was used to perform the biomarker-based dose-response analysis, and various other approaches to the dose-response analysis were conducted for comparison. The network-derived benchmark concentration was approximately an order of magnitude lower than that from the usual exposure concentration versus response approach, which suggests that the presence of more information in the low-dose region (where changes in biomarkers are detectable but effects on AML mortality are not) helps inform the description of the AML response at lower exposures. This work provides a quantitative approach for linking changes in biomarkers of effect both to exposure information and to changes in disease response. Such linkage can provide a scientifically valid point of departure that incorporates precursor dose-response information without being dependent on the difficult issue of a definition of adversity for precursors.

Internal Threshold of Toxicological Concern (iTTC): Where We Are Today and What Is Possible in the Near Future.

The Threshold of Toxicological Concern (TTC) is a risk assessment tool for evaluating low-level exposure to chemicals with limited toxicological data. A next step in the ongoing development of TTC is to extend this concept further so that it can be applied to internal exposures. This refinement of TTC based on plasma concentrations, referred to as internal TTC (iTTC), attempts to convert the chemical-specific external NOAELs (in mg/kg/day) in the TTC database to an estimated internal exposure. A multi-stakeholder collaboration formed, with the aim of establishing an iTTC suitable for human safety risk assessment. Here, we discuss the advances and future directions for the iTTC project, including: (1) results from the systematic literature search for metabolism and pharmacokinetic data for the 1,251 chemicals in the iTTC database; (2) selection of ~350 chemicals that will be included in the final iTTC; (3) an overview of the in vitro caco-2 and in vitro hepatic metabolism studies currently being generated for the iTTC chemicals; (4) demonstrate how PBPK modeling is being utilized to convert a chemical-specific external NOAEL to an internal exposure; (5) perspective on the next steps in the iTTC project.

A pharmacokinetic model of the intracellular dosimetry of inhaled nickel.

The potential associations between exposure to nickel compounds and cancer have been evaluated in both animal and epidemiological studies of occupationally exposed workers. The results of the epidemiological studies suggest that not all nickel compounds are equally carcinogenic, an observation supported by the animal bioassay results. Given the complexity and the differences in the modes of uptake of different forms of nickel by cells and the subsequent delivery of nickel to the nucleus, it would be expected that some forms of nickel would be more potent than others. A physiologically based pharmacokinetic (PBPK) model would be useful in estimating the cellular exposure to nickel resulting from inhalation of the different forms of nickel. To this end, a preliminary model of a tracheobronchial epithelial cell was developed to describe the differences in the extracellular and intracellular kinetics of the different classes of nickel compounds. Data available in the published literature were used to define the initial model parameters. The resulting cellular dosimetry model was able to describe kinetic data on three forms of nickel (soluble chloride and insoluble sulfide and subsulfide). This preliminary model development effort has identified critical data gaps that could be filled by additional research. The ultimate goal will be to integrate a refined cellular dosimetry model with published lung deposition/clearance and systemic distribution/clearance models for nickel. The use of such an integrated PBPK model would allow for more biologically based risk estimates for the inhalation of the different nickel compounds, as well as mixtures of these compounds.

Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.

Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach.

An approach for the quantitative consideration of genetic polymorphism data in chemical risk assessment: examples with warfarin and parathion.

In recent years, a great deal of research has been conducted to identify genetic polymorphisms. One focus has been to characterize variability in metabolic enzyme systems that could impact internal doses of pharmaceuticals or environmental pollutants. Methods are needed for using this metabolic information to estimate the resulting variability in tissue doses associated with chemical exposure. We demonstrate here the use of physiologically based pharmacokinetic (PBPK) modeling in combination with Monte Carlo analysis to incorporate information on polymorphisms into the analysis of toxicokinetic variability. Warfarin and parathion were used as case studies to demonstrate this approach. Our results suggest that polymorphisms in the PON1 gene, that give rise to allelic variants of paraoxonase, which is involved in the metabolism of paraoxon (a metabolite of parathion), make only a minor contribution to the overall variability in paraoxon tissue dose, while polymorphisms in the CYP2C9 gene, which gives rise to allelic variants of the major metabolic enzyme for warfarin, account for a significant portion of the overall variability in (S)-warfarin tissue dose. These analyses were used to estimate chemical-specific adjustment factors (CSAFs) for the human variability in toxicokinetics for both parathion and warfarin. Implications of alternatives in the calculation of CSAFs are explored. Key decision points for applying the PBPK-Monte Carlo approach to evaluate toxicokinetic variability for other chemicals are also discussed.

Predicting passive and active tissue:plasma partition coefficients: interindividual and interspecies variability.

A mechanistic tissue composition model incorporating passive and active transport for the prediction of steady-state tissue:plasma partition coefficients (K(t:pl)) of chemicals in multiple mammalian species was used to assess interindividual and interspecies variability. This approach predicts K(t:pl) using chemical lipophilicity, pKa, phospholipid membrane binding, and the unbound plasma fraction, together with tissue fractions of water, neutral lipids, neutral and acidic phospholipids, proteins, and pH. Active transport K(t:pl) is predicted using Michaelis-Menten transport parameters. Species-specific biological properties were identified from 126 peer reviewed journal articles, listed in the Supporting Information, for mouse, rat, guinea pig, rabbit, beagle dog, pig, monkey, and human species. Means and coefficients of variation for biological properties were used in a Monte Carlo analysis to assess variability. The results show K(t:pl) interspecies variability for the brain, fat, heart, kidney, liver, lung, muscle, red blood cell, skin, and spleen, but uncertainty in the estimates obscured some differences. Compounds undergoing active transport are shown to have concentration-dependent K(t:pl). This tissue composition-based mechanistic model can be used to predict K(t:pl) for organic chemicals across eight species and 10 tissues, and can be an important component in drug development when scaling K(t:pl) from animal models to humans.

Use of Markov Chain Monte Carlo analysis with a physiologically-based pharmacokinetic model of methylmercury to estimate exposures in US women of childbearing age.

A Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC) analysis, was applied with a physiologically-based pharmacokinetic (PBPK) model of methylmercury (MeHg) to evaluate the variability of MeHg exposure in women of childbearing age in the U.S. population. The analysis made use of the newly available National Health and Nutrition Survey (NHANES) blood and hair mercury concentration data for women of age 16-49 years (sample size, 1,582). Bayesian analysis was performed to estimate the population variability in MeHg exposure (daily ingestion rate) implied by the variation in blood and hair concentrations of mercury in the NHANES database. The measured variability in the NHANES blood and hair data represents the result of a process that includes interindividual variation in exposure to MeHg and interindividual variation in the pharmacokinetics (distribution, clearance) of MeHg. The PBPK model includes a number of pharmacokinetic parameters (e.g., tissue volumes, partition coefficients, rate constants for metabolism and elimination) that can vary from individual to individual within the subpopulation of interest. Using MCMC analysis, it was possible to combine prior distributions of the PBPK model parameters with the NHANES blood and hair data, as well as with kinetic data from controlled human exposures to MeHg, to derive posterior distributions that refine the estimates of both the population exposure distribution and the pharmacokinetic parameters. In general, based on the populations surveyed by NHANES, the results of the MCMC analysis indicate that a small fraction, less than 1%, of the U.S. population of women of childbearing age may have mercury exposures greater than the EPA RfD for MeHg of 0.1 microg/kg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 microg/kg/day. The analysis also indicates that typical exposures may be greater than previously estimated from food consumption surveys, but that the variability in exposure within the population of U.S. women of childbearing age may be less than previously assumed.

Bayesian population analysis of a harmonized physiologically based pharmacokinetic model of trichloroethylene and its metabolites.

Bayesian population analysis of a harmonized physiologically based pharmacokinetic (PBPK) model for trichloroethylene (TCE) and its metabolites was performed. In the Bayesian framework, prior information about the PBPK model parameters is updated using experimental kinetic data to obtain posterior parameter estimates. Experimental kinetic data measured in mice, rats, and humans were available for this analysis, and the resulting posterior model predictions were in better agreement with the kinetic data than prior model predictions. Uncertainty in the prediction of the kinetics of TCE, trichloroacetic acid (TCA), and trichloroethanol (TCOH) was reduced, while the kinetics of other key metabolites dichloroacetic acid (DCA), chloral hydrate (CHL), and dichlorovinyl mercaptan (DCVSH) remain relatively uncertain due to sparse kinetic data for use in this analysis. To help focus future research to further reduce uncertainty in model predictions, a sensitivity analysis was conducted to help identify the parameters that have the greatest impact on various internal dose metric predictions. For application to a risk assessment for TCE, the model provides accurate estimates of TCE, TCA, and TCOH kinetics. This analysis provides an important step toward estimating uncertainty of dose-response relationships in noncancer and cancer risk assessment, improving the extrapolation of toxic TCE doses from experimental animals to humans.

Revised assessment of cancer risk to dichloromethane: part I Bayesian PBPK and dose-response modeling in mice.

The current USEPA cancer risk assessment for dichloromethane (DCM) is based on deterministic physiologically based pharmacokinetic (PBPK) modeling involving comparative metabolism of DCM by the GST pathway in the lung and liver of humans and mice. Recent advances in PBPK modeling include probabilistic methods and, in particular, Bayesian inference to quantitatively address variability and uncertainty separately. Although Bayesian analysis of human PBPK models has been published, no such efforts have been reported specifically addressing the mouse, apart from results included in the OSHA final rule on DCM. Certain aspects of the OSHA model, however, are not consistent with current approaches or with the USEPA's current DCM cancer risk assessment. Therefore, Bayesian analysis of the mouse PBPK model and dose-response modeling was undertaken to support development of an improved cancer risk assessment for DCM. A hierarchical population model was developed and prior parameter distributions were selected to reflect parameter values that were considered the most appropriate and best available. Bayesian modeling was conducted using MCSim, a publicly available software program for Markov Chain Monte Carlo analysis. Mean posterior values from the calibrated model were used to develop internal dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day in the lung and liver using exposure concentrations and results from the NTP mouse bioassay, consistent with the approach used by the USEPA for its current DCM cancer risk assessment. Internal dose metrics were 3- to 4-fold higher than those that support the current USEPA IRIS assessment. A decrease of similar magnitude was also noted in dose-response modeling results. These results show that the Bayesian PBPK model in the mouse provides an improved basis for a cancer risk assessment of DCM.