Curtis C. Dary

RECONSTRUCTING HUMAN EXPOSURES USING BIOMARKERS AND OTHER "CLUES"

Biomonitoring is the process by which biomarkers are measured in human tissues and specimens to evaluate exposures. Given the growing number of population-based biomonitoring surveys, there is now an escalated interest in using biomarker data to reconstruct exposures for supporting risk assessment and risk management. While detection of biomarkers is de facto evidence of exposure and absorption, biomarker data cannot be used to reconstruct exposure unless other information is available to establish the external exposure–biomarker concentration relationship. In this review, the process of using biomarker data and other information to reconstruct human exposures is examined. Information that is essential to the exposure reconstruction process includes (1) the type of biomarker based on its origin (e.g., endogenous vs. exogenous), (2) the purpose/design of the biomonitoring study (e.g., occupational monitoring), (3) exposure information (including product/chemical use scenarios and reasons for expected contact, the physicochemical properties of the chemical and nature of the residues, and likely exposure scenarios), and (4) an understanding of the biological system and mechanisms of clearance. This review also presents the use of exposure modeling, pharmacokinetic modeling, and molecular modeling to assist in integrating these various types of information.

Role of Laboratory Sampling Devices and Laboratory Subsampling Methods in Representative Sampling Strategies

Sampling is the act of selecting items from a specified population in order to estimate the parameters of that population (e.g., selecting soil samples to characterize the properties at an environmental site). Sampling occurs at various levels and times throughout an environmental site characterization process. Typically, initial (primary) sampling occurs in the field while subsequent stages of sample size reduction (subsampling) occur until the final laboratory analysis stage. At each step in the measurement process, from planning, site selection, sample collection, sample preparation, through sample analysis, errors can occur that propagate, leading to uncertainty associated with the final result upon which decisions will ultimately be made. The goal of all sampling efforts should be to select samples that are representative of the population (i.e., site) in question. General guidelines, with supporting background and theory, for obtaining representative subsamples for the laboratory analysis of particulate materials using “correct” sampling practices and “correct” sampling devices are presented (“correct” as defined by Gy sampling theory; see Pitard, 1993). Considerations are given to: the constitution and the degree of heterogeneity of the material being sampled, the methods used for sample collection (including what proper tools to use), what it is that the sample is supposed to represent, the mass of the sample needed to be representative, and the bounds of what “representative” actually means.

Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

In this review we have examined the status of parameters required by pyrethroid QSAR-PBPK/PD models for assessing health risks. In lieu of the chemical,biological, biochemical, and toxicological information developed on the pyrethroids since 1968, the finding of suitable parameters for QSAR and PBPK/PD model development was a monumental task. The most useful information obtained came from rat toxicokinetic studies (i.e., absorption, distribution, and excretion), metabolism studies with 14C-cyclopropane- and alcohol-labeled pyrethroids, the use of known chiral isomers in the metabolism studies and their relation to commercial products. In this review we identify the individual chiralisomers that have been used in published studies and the chiral HPLC columns available for separating them. Chiral HPLC columns are necessary for isomer identification and for developing kinetic values (Vm,, and Kin) for pyrethroid hydroxylation. Early investigators synthesized analytical standards for key pyrethroid metabolites, and these were used to confirm the identity of urinary etabolites, by using TLC. These analytical standards no longer exist, and muste resynthesized if further studies on the kinetics of the metabolism of pyrethroids are to be undertaken.In an attempt to circumvent the availability of analytical standards, several CYP450 studies were carried out using the substrate depletion method. This approach does not provide information on the products formed downstream, and may be of limited use in developing human environmental exposure PBPK/PD models that require extensive urinary metabolite data. Hydrolytic standards (i.e., alcohols and acids) were available to investigators who studied the carboxylesterase-catalyzed hydrolysis of several pyrethroid insecticides. The data generated in these studies are suitable for use in developing human exposure PBPK/PD models.Tissue:blood partition coefficients were developed for the parent pyrethroids and their metabolites, by using a published mechanistic model introduced by Poulin and Thiele (2002a; b) and log DpH 7.4 values. The estimated coefficients, especially those of adipose tissue, were too high and had to be corrected by using a procedure in which the proportion of parent or metabolite residues that are unbound to plasma albumin is considered, as described in the GastroPlus model (Simulations Plus, Inc.,Lancaster, CA). The literature suggested that Km values be adjusted by multiplying Km by the substrate (decimal amount) that is unbound to microsomal or CYPprotein. Mirfazaelian et al. (2006) used flow- and diffusion-limited compartments in their deltamethrin model. The addition of permeability areas (PA) having diffusion limits, such as the fat and slowly perfused compartments, enabled the investigators to bring model predictions in line with in vivo data.There appears to be large differences in the manner and rate of absorption of the pyrethroids from the gastrointestinal tract, implying that GI advanced compartmental transit models (ACAT) need to be included in PBPK models. This is especially true of the absorption of an oral dose of tefluthrin in male rats, in which 3.0-6.9%,41.3-46.3%, and 5.2-15.5% of the dose is eliminated in urine, feces, and bile,respectively (0-48 h after administration). Several percutaneous studies with the pyrethroids strongly support the belief that these insecticides are not readily absorbed, but remain on the surface of the skin until they are washed off. In one articular study (Sidon et al. 1988) the high levels of permethrin absorption through the forehead skin (24-28%) of the monkey was reported over a 7- to 14-days period.Wester et al. (1994) reported an absorption of 1.9% of pyrethrin that had been applied to the forearm of human volunteers over a 7-days period.SAR models capable of predicting the binding of the pyrethroids to plasma and hepatic proteins were developed by Yamazaki and Kanaoka (2004), Saiakhov et al. (2000), Colmenarejo et al. (2001), and Colmenarejo (2003). QikProp(Schrodinger, LLC) was used to obtain Fu values for calculating partition coefficients and for calculating permeation constants (Caco-2, MDCK, and logBBB). ADMET Predictor (Simulations Plus Inc.) provided Vm~,x and Km values for the hydroxylation of drugs/pyrethroids by human liver recombinant cytochrome P450 enzymes making the values available for possible use in PBPK/PD models.The Caco-2 permeability constants and CYP3A4 Vmax and Km values are needed in PBPK/PD models with GI ACAT sub models. Modeling work by Chang et al.(2009) produced rate constants (kcat) for the hydrolysis of pyrethroids by rat serumcarboxylesterases. The skin permeation model of Potts and Guy (1992) was used topredict K, values for the dermal absorption of the 15 pyrethroids.The electrophysiological studies by Narahashi (1971) and others (Breckenridgeet al. 2009; Shafer et al. 2005; Soderlund et al. 2002; Wolansky and Harrill 2008)demonstrated that the mode of action of pyrethroids on nerves is to interfere with the changes in sodium and potassium ion currents. The pyrethroids, being highly lipid soluble, are bound or distributed in lipid bilayers of the nerve cell membrane and exert their action on sodium channel proteins. The rising phase of the action potential is caused by sodium influx (sodium activation), while the falling phase is caused by sodium activation being turned off, and an increase in potassium efflux(potassium activation). The action of allethrin and other pyrethroids is caused by an inhibition or block of the normal currents. An equation by Tatebayashi and Narahashi (1994) that describes the action of pyrethroids on sodium channels was found in the literature. This equation, or some variation of it, may be suitable for use in the PD portion of pyrethroid PBPK models.

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

PAVA: physiological and anatomical visual analytics for mapping of tissue-specific concentration and time-course data

We describe the development and implementation of a Physiological and Anatomical Visual Analytics tool (PAVA), a web browser-based application, used to visualize experimental/simulated chemical time-course data (dosimetry), epidemiological data and Physiologically-Annotated Data (PAD). Using continuous color mapping scheme both spatial (organ shape and location) and temporal (time-course/kinetics) data was cast onto an abstract, layered, 2D visual representation of the human anatomy and physiology. This approach is aligned with the compartment-level of detail afforded by Physiologically-Based Pharmacokinetic (PBPK) modeling of chemical disposition. In this tutorial we provide several illustrative examples of how PAVA may be applied: (1) visualization of multiple organ/tissue simulated dosimetry of a previously published oral exposure route ethanol PBPK model, (2) visualization of PAD such as organ-specific disease time-lines or (3) tissue-specific mRNA expression-level profiles (e.g. phase I/II metabolic enzymes and nuclear receptors) to draw much needed molecular biological conclusions at organ-level resolution conducive to model development. Furthermore, discussion is raised on how graphical representations of PBPK models, and the use of PAVA more generally to visualize PAD, can be of benefit. We believe this novel platform-independent tool for visualizing PAD on physiologically-relevant representations of human anatomy will become a valuable visual analytic addition to the tool-kits of modern exposure scientists, computational biologists, toxicologists, biochemists, molecular biologists, epidemiologists and pathologists alike in visually translating, representing and mining complex PAD relationships required to understand systems biology or manage chemical risk.

Determination of the Distribution of Malathion in Rats Following Various Routes of Administration by Whole-Body Electronic Autoradiography

The distribution of [14C]-malathion, an organophosphorus pesticide, in rats after intravenous, oral and dermal administration was carried out using electronic autoradiography of whole body sections of treated animals. The study indicated that a major difference in the disposition of [14C]-malathion occurred following various routes of administration to rats. Following intravenous administration, the liver and kidney accumulated extremely high levels of the chemical. After oral administration, [14C]-malathion absorption from the stomach was slow and its excretion followed mostly the fecal route. Dermal application of [14C]-malathion may represent a high risk for exposure to the organophosphorus pesticide where the entire skin, not only the site of application, may act as reservoir for the compound.

Informing Mechanistic Toxicology with Computational Molecular Models

Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo efforts. From a molecular biophysical ansatz, we describe how 3D molecular modeling methods used to numerically evaluate the classical pair-wise potential at the chemical/biological interface can inform mechanism of action and the dose-response paradigm of modern toxicology. With an emphasis on molecular docking, 3D-QSAR and pharmacophore/toxicophore approaches, we demonstrate how these methods can be integrated with chemoinformatic and toxicogenomic efforts into a tiered computational toxicology workflow. We describe generalized protocols in which 3D computational molecular modeling is used to enhance our ability to predict and model the most relevant toxicokinetic, metabolic, and molecular toxicological endpoints, thereby accelerating the computational toxicology-driven basis of modern risk assessment while providing a starting point for rational sustainable molecular design.

Biomarkers in computational toxicology

Biomarkers are a means to evaluate chemical exposure and/or the subsequent impacts on toxicity pathways that lead to adverse health outcomes. Computational toxicology can integrate biomarker data with knowledge of exposure, chemistry, biology, pharmacokinetics, toxicology, and epidemiology to inform the linkages among exposure, susceptibility, and human health. This chapter provides an overview of four computational modeling approaches and their applications for interpreting biomarker data. Exposure models integrate the microenvironmental concentrations with human activity data to estimate intake doses. Dosimetry models incorporate mechanistic biological information to link intake doses to biomarkers. Biologically plausible models describe normal and xenobiotic-perturbed behaviors that can be distinguished using biomarkers. Cheminformatics-based models provide rapid assessments to inform future biomarker studies. Together, these modeling approaches allow for comprehensive investigations of biomarker data to link between exposures and disease.

Computational Toxicology: Application in Environmental Chemicals

This chapter provides an overview of computational models that describe various aspects of the source-to-health effect continuum. Fate and transport models describe the release, transportation, and transformation of chemicals from sources of emission throughout the general environment. Exposure models integrate the microenvironmental concentrations with the amount of time an individual spends in these microenvironments to estimate the intensity, frequency, and duration of contact with environmental chemicals. Physiologically based pharmacokinetic (PBPK) models incorporate mechanistic biological information to predict chemical-specific absorption, distribution, metabolism, and excretion. Values of parameters in PBPK models can be measured in vitro, in vivo, or estimated using computational molecular modeling. Computational modeling is also used to predict the respiratory tract dosimetry of inhaled gases and particulates [computational fluid dynamics (CFD) models], to describe the normal and xenobiotic-perturbed behaviors of signaling pathways, and to analyze the growth kinetics of preneoplastic lesions and predict tumor incidence (clonal growth models).

Chapter 73 – Application of Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling in Cumulative Risk Assessment for N-Methyl Carbamate Insecticides

Publisher Summary Cumulative risk assessment (CRA) is needed in order to evaluate the net cumulative toxicity caused by the aggregate exposure from all routes of entry for a single chemical or a group of chemicals that have a common mechanism of toxicity. The application of pesticides for the purpose of insect pest control creates such possible scenarios, not only in occupational settings but also in the general population. Organophosphorus compounds, N-methyl carbamates (NMCs), and pyrethroids are three popular classes of insecticides widely used in the United States and worldwide. As insecticides, N-methyl carbamates (NMCs) share a common chemical structure with the general formula ROC(O)NHCH3 for N-methyl carbamates and ROC(O)N(CH3)2 for dimethyl carbamates. A CRA begins with the identification of a CMG of chemicals, which exert toxic effects by a common mechanism of action. There are four methodologies that include a toxicological index method, a margin of exposure method, a relative potency factor (RPF) method, and physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling. The index method accounts for cumulative risk by summing all risk indexes calculated as the ratio of exposure level to the reference value for each individual chemical. PBPK models can be regarded as the “electronic copy” of the laboratory animal or human test system. “Exposure” can be simulated in silico and tissue dosimetry can be estimated or predicted prior to further animal testing. The PBPK/PD modeling approach can simulate the exposure in a more pharmacokinetic fashion and make predictions on the toxicological endpoints. But models built for such a purpose need to be of higher quality and must be constructed with diversified experimental data, model calibration/validation, and uncertainty analysis.