[Progress in Molecular Biology and Translational Science] Toxicology and Human Environments Volume...

CHAPTER SIX

Occupational Chemicals:Metabolism, Toxicity, and Mode ofActionSheila Flack, Leena A. Nylander-FrenchDepartment of Environmental Sciences and Engineering, Gillings School of Global Public Health, Universityof North Carolina at Chapel Hill, CB# 7431, Rosenau Hall, Chapel Hill, North Carolina, USA

Contents

1.

ProgISShttp

Introduction

ress in Molecular Biology and Translational Science, Volume 112 # 2012 Elsevier Inc.N 1877-1173 All rights reserved.://dx.doi.org/10.1016/B978-0-12-415813-9.00006-4

164
2. Structure and Function of Biological Membranes 167
2.1
Lungs 167 2.2 Skin 168 2.3 Eyes 171 2.4 Gastrointestinal tract 172
3.
Quantitative Approaches in Exposure Assessment 173 3.1 Occupational versus environmental exposures 173 3.2 Inhalation exposure 174 3.3 Dermal exposure 174 3.4 Ingestion exposure 175 3.5 Exposure and dose metrics 175
4.
Integrating Biological Monitoring and Toxicology into Risk Assessment 177 4.1 Biomarker selection 177 4.2 Biomarker validation 181 4.3 Exposure modeling 183 4.4 Exposure–dose modeling 186 4.5 Toxicokinetics and toxicodynamics 188
5.
Emerging Issues and Technologies 191 5.1 Variability and uncertainty 191 5.2 Gene–environment interaction 192 5.3 In vitro models in risk assessment 195
6.
Research Needs and Data Gaps 197 6.1 Developing and conducting exposure monitoring studies 197 6.2 Interpreting biological monitoring studies 198 6.3 Evaluating toxicogenomic studies for risk assessment 199
7.
Concluding Remarks 200 References 201
163

http://dx.doi.org/10.1016/B978-0-12-415813-9.00006-4

164 Sheila Flack and Leena A. Nylander-French

Abstract

Workers experience large interindividual variability in exposure and biological responsefollowing exposure to chemicals. Quantitative methods to investigate occupationalexposures and their relationship with biomarker levels, toxicokinetics of chemicals,and gene–environment interactions in disease development can be performed to un-lock the black-box paradigm in exposure–disease associations. Exposure to a chemicalat work is generally greater than that experienced in the wider environment. While in-halation exposure has traditionally been the main focus in exposure assessment, there isgrowing awareness of the significance of contact and uptake of chemicals throughdermal and ingestion routes. Biological monitoring can provide information on expo-sure and uptake of a chemical, biological response to exposure, early subclinicalchanges, and susceptibility for disease. Thus, biomarkers can provide an important linkbetween exposure and disease and may be an important tool for risk assessment. In-tegration of toxicology with exposure assessment, dose–response, and toxicogenomicscan be used to improve one's understanding of exposure–disease relationships andshape risk assessment strategies to protect worker health.

1. INTRODUCTION

In order to understand the causes of diseases in human populations,

associations are sought between various exposures and disease using an

approach termed “black-box” epidemiology. The hypothesized causal link

between exposure and disease is termed “black box” because the causal link

is unknown (“black”) but its existence is implied (“box”).1 One important

limitation of the black-box approach is that it identifies risk factors but not an

explanatory theory for how disease arises, which limits the development of

effective prevention and intervention strategies.2 Therefore, a complemen-

tary “mechanistic” approach is required to identify molecular-based mech-

anisms for occupational disease development, such as asbestosis and silicosis.3

By integrating these two approaches, a broader conceptualization of the

exposure–disease framework (i.e., systems theory approach) can be utilized

to unlock the black-box paradigm.4 This more expansive view changes the

simplistic black-box approach and allows for development of more compre-

hensive investigations on disease etiology and methodologies for evaluating

prevention and intervention strategies.

The mechanistic approach to determine how an exposure to a chemical

produces adverse effects in humans, in both occupational and environmental

settings, is based in toxicology research. Therefore, the main goal of toxi-

cology is to establish causal links between exposure and disease, and thus,

165Toxicology of Occupational Chemicals

toxicology plays a fundamental role in risk assessment. Establishing causal links

is achieved not only through conducting dose–response analyses in laboratory

animals to obtain toxicity estimates but also by investigating how toxicants are

absorbed, distributed, and metabolized in animal models and scaled to the

human body. The bioavailability of toxicants depends upon the exposure

level, rate of uptake into systemic circulation, retention or residence time

in storage tissues, biotransformation into water-soluble compounds, and elim-

ination from the body. Interindividual differences in how various toxicants are

metabolized or in DNA repair capacity and efficiency based on genotypic

polymorphisms may influence individual susceptibility to disease. Conse-

quently, large uncertainties exist along the exposure–dose continuum that will

influence interpretation of human health risks.

Biological monitoring, in conjunction with exposure monitoring, is fre-

quently applied in occupational exposure assessment to reduce uncertainties in

exposure and risk assessment. Biological monitoring involves a direct mea-

surement of biomarker levels in human specimens (e.g., blood, urine, saliva).

The concomitant quantification of exposure and biomarker levels can provide

information regarding variability and time trends in exposure.5 Biomarker

data can be included in a variety of computational models to evaluate exposure

at the individual or population level and be used to predict the incidence or

outcome of disease.6,7 Biomarkers provide a better understanding of exposure

factors, permit the estimation of biological effective dose arising from

exposure, provide a more sensitive assessment of effects, allow for earlier

recognition of health outcomes with long latency periods, and set priorities

for research on human health effects.8 A biological monitoring framework

can be developed and used as a guide to interpret biomarker data, identify

knowledge gaps, develop new studies to answer exposure and risk-based

questions, prioritize research needs, and integrate knowledge across

multiple scientific disciplines to improve understanding of human health

risks.6 Consequently, biological monitoring data can be used to meet one

of the most critical objectives of risk assessment: to determine whether

individuals or a population are at increased risk of experiencing adverse

health effects associated with an exposure to a specific substance. For these

reasons, biological monitoring is quickly becoming the “gold standard” of

environmental exposure assessment.9

A great strength of biological monitoring data is that it represents an

integration of exposure from all sources and routes, which provides an im-

portant perspective on overall exposure.5 However, measuring biomarkers

alone does not provide adequate information for calculating health risks. For


any particular chemical exposure, risk assessment can be summarized with

the equation

Risk¼Toxicity�Exposure: ½6:1�Toxicity studies are necessary to identify a hazard and to establish a

relationship between administered dose and health effects. The use of phys-

iologically based pharmacokinetic (PBPK) modeling supports the interpre-

tation of biological monitoring data in the context of exposure

reconstruction and risk characterization. Aided with toxicokinetic knowl-

edge, biological monitoring data can represent short-term exposure or a cu-

mulative internal dose due to repeated exposure. PBPK models have been

used to improve understanding of absorption, distribution, metabolism, and

excretion (ADME) processes for a variety of chemicals and often work in

conjunction with studies on their mode of action (i.e., biochemical interac-

tions that produce their toxic effect). Hence, toxicity and toxicokinetic stud-

ies provide the means to unlock black-box epidemiology by linking

exposure and biological monitoring data to health outcomes and, therefore,

enable improved calculation of risk in exposed populations (Fig. 6.1).

- Intra- and interindividual variation




- Toxicity studies

- Toxicogenomics- Toxicokinetic modeling

Exposure

Biological Monitoring

Toxicology research

Disease

“Black Box”

Figure 6.1 Approaches to unlock “black-box” epidemiology to better informexposure–disease associations.


The objective of this chapter is to describe how toxicology can be uti-

lized in the context of occupational exposure assessment to better inform

individual susceptibility and disease risk within an exposed population.

The biological membranes through which chemicals enter the body are

described and metabolism within each type of membrane is compared. In

addition, the current knowledge base for exposure characterization, biolog-

ical monitoring strategies, PBPKmodeling, animal toxicity, and the integra-

tion of these studies is presented to determine human health risk. Lastly,

several important current and future areas of research and technologies that

have emerged as a result of advances in genomic research, bioinformatics,

and computational tools, which have created exciting possibilities for new

applications of toxicology in human health risk assessment, are summarized.

2. STRUCTURE AND FUNCTION OF BIOLOGICALMEMBRANES

2.1. Lungs
The lungs are the main organs of the respiratory system and serve several
important functions: (1) gas exchange (oxygen and carbon dioxide), (2)

removal of waste and toxins, and (3) defense against infection and harmful

substances (e.g., dirt, bacteria, viruses). The lungs are the largest organ of the

respiratory system (80 m2), which enables easy and efficient exchange of ox-

ygen and carbon dioxide between the body and the environment. Approx-

imately 10% of the lung volume is made up of solid tissue, while the

remainder is filled with air and blood. The functional structure of the lungs

can be divided into the gas exchange region and the conducting airways

(dead space). Gas exchange occurs in the alveolus, where blood flow and

inspired air are separated only by a thin tissue layer. During inspiration,

air enters the conducting airways reaching the alveoli where oxygen is ex-

changed for carbon dioxide through simple diffusion. During expiration,

carbon dioxide and other waste products diffuse out to the external environ-

ment. The body’s entire blood volume passes through the lungs each minute

in the resting state (approximately 5 l/min).

In addition to gas exchange, the lungs have a number of metabolic func-

tions. Bronchial epithelial cells, such as Clara cells, are known to be a pri-

mary defense against pulmonary toxicants. These epithelial cells express

chemical-metabolizing enzymes that are involved in the biotransformation

of inhaled environmental chemicals10 through phase 1 and 2 metabolism

(Fig. 6.2). Phase 1 involves activation of the parent compound by oxidizing

Elimination

EliminationReaction with DNA

Inactive metabolites

Inactive metabolitesActive metabolitesXenobioticPhase 1 Phase 2

Phase 1

Figure 6.2 Metabolism of xenobiotics to inactive and active metabolites by phase 1 en-zymes (predominately CYP family) via oxidation and to inactive metabolites by phase 2enzymes via conjugation (e.g., by glucuronidation, sulfonation).


enzymes, such as cytochrome P450 (CYP)-dependent enzymes, to highly

reactive intermediates. In phase 2, the highly reactive intermediates may

bemetabolized by other enzymes, such as transferases, to more water-soluble

metabolites that can leave the cells. The major chemical-metabolizing

enzymes expressed in bronchial epithelial cells are the phase 1 CYP superfam-

ily of enzymes, which catalyze the oxidative metabolism and metabolic acti-

vation of chemical compounds in the lung.11 Phase 2 enzymes, such as

glutathione-S-transferases (GST) and N-acetyltransferases (NAT), are also

expressed in bronchial epithelial cells and aremainly considered to be involved

in the detoxification of several chemical compounds. These phase 2 chemical-

metabolizing enzymes may also, in certain conditions, contribute to toxic

pathways. For example, Clara cells have been shown to be important in

the detoxification of styrene,12 a chemical that is widely used in the plastics

industry with extensive exposure and possible risk of cancer. In the lung,

impairment of NAT activity by oxidative damage, such as air pollution,

may alter biotransformation of aromatic amines and result in important tox-

icological consequences.13 Therefore, these studies demonstrate the important

role of the lungs in chemical uptake, metabolism, and detoxification.

2.2. SkinThe skin is the largest human organ by weight (about 16% of the body

weight) and serves as an anatomical barrier. The skin is also an important

organ for containing chemical metabolism and dynamic immune response

systems. It was long viewed only as a passive physical barrier between the

body and the environment. But within the last few decades, skin has been

shown to possess significant metabolic capacity due to enzymes located


mainly in the epidermis. Many chemicals can be absorbed and metabolized

in the skin where they can interact with nucleophilic macromolecules and/

or components of the skin immune system.While the skin is recognized as a

target for environmental and occupational chemical exposures, very little is

known about the basic functions of chemical metabolism in human skin and

its role in mediating drug/chemical-induced adverse effects (e.g., allergy,

cancer).14

Human skin is composed of two primary layers: (1) the epidermis, which

provides the barrier and metabolic functions, and (2) the dermis, a highly

vascularized layer that is the location for the appendages of the skin (i.e., hair

follicles, sweat glands, sebaceous and apocrine glands, lymphatic vessels, and

blood vessels). The epidermis, consisting of the stratum corneum (outer

layer) and the viable epidermis (inner layer), has complex and diverse func-

tions. The stratum corneum forms the waterproof, protective barrier and is

made up of 20–30 thin layers of continually shedding, dead keratinocytes.

The viable epidermis is composed of four layers: (1) stratum lucidum (trans-

lucent layer), (2) stratum granulosum (granular layer), (3) stratum spinosum

(spinous layer), and (4) stratum basale (innermost, basal layer). The epithe-

lium is maintained by cell division within the basal layer, and the differen-

tiated cells are displaced through the epidermal layers. In the stratum

corneum, the cells lose their nucleus and fuse to form squamous sheets,

which are eventually shed from the surface. The entire epidermis is replaced

by new cell growth over a period of about 48 days.

Three important cell types of the epidermis are melanocytes, Merkel

cells, and Langerhans’ cells. Skin color is due to the production of melanin

by melanocytes located in the stratum basale. Merkel cells, which are clear

cells found in the stratum basale, are involved in detecting somatosensory

stimuli (e.g., pain, temperature, touch). The Langerhans’ cell is found in

the upper stratum spinosum and is part of the immune system. In skin

exposure and infections, Langerhans’ cells take up and process microbial

antigens to become fully functional antigen-presenting cells. Once in the

lymphoid tissue, the antigen-presenting cells activate T-cells, facilitating

immune response.

In addition to structural barrier and immune response function, there is

increasing evidence for a biochemical barrier function of the skin. While the

liver is considered the principal organ of chemical and xenobiotic metabo-

lism, the metabolic capabilities of the skin must not be discounted. In human

skin, chemicalmetabolismoccurs through at least three phases. Phases 1 and 2

are involved in activation and transformation to water-soluble metabolites,


respectively (Fig. 6.2). Phase 3 is mediated by influx and efflux transporter

proteins that are present in cutaneous cells, such as keratinocytes and

antigen-presenting cells. Therefore, skin provides the capability for active

uptake, biotransformation, and transport of a range of chemicals including

drugs, solvents, and carcinogens.

In Table 6.1, a skin cocktail of CYP enzymes is compared with a liver

CYP cocktail to reflect differences in metabolic capacity in these two sys-

tems (e.g., CYP1B1 and CYP2B6 are present in skin but not in the liver).15

CYP1B1 activates various environmental carcinogens, such as polycyclic

aromatic hydrocarbons (PAHs), while CYP2B6 metabolizes several

chemicals, such as nicotine and some anticancer drugs. CYP1A1 and

CYP1A2 play prominent roles in the metabolic activation of carcinogenic

PAHs and heterocyclic aromatic amines/amides, respectively, to reactive in-

termediates, leading to toxicity and cancer.16 Tissue-specific expression and

Table 6.1 Comparison of cytochrome P450 isoenzymes and compounds that theymetabolize in the liver versus skin cytochrome P450 cocktaila

CYPisoenzyme

Liver(pmol)

Skin(pmol) Xenobiotics that are metabolized

1A1 – 3.6 PAHs (e.g., benzo[a]pyrene)

1A2 20 – PAHs (e.g., compounds in cigarette smoke)

1B1 – 2.0 Procarcinogens (e.g., PAHs)

2B6 – 0.035 Nicotine, anticancer drugs (cyclophosphamide,

ifosphamide)

2C9 17 – Therapeutic drugs (e.g., warfarin, phenytoin, losartan)

2C19 8 – Anticonvulsive drugs (e.g., mephenytoin, diazepam,

omeprazole)

2D6 3 – Analgesics (e.g., codeine, dihydrocodeine, tramadol)

2E1 11 11 Anesthetics, ethanol, industrial toxins (e.g., benzene,

dimethylformamide)

3A4 45 – Many drugs (e.g., acetaminophen, codeine, diazepam,

erythromycin), some steroids and carcinogens

3A5 – 5.6 Therapeutic drugs (e.g., nifediprine, cyclosporine)

Total 104 22

aFindings from Merk.15

PAH, polycyclic aromatic hydrocarbon; CYP, cytochrome P450.


metabolism of chemicals could lead to the relative proportion of reactive

metabolites produced in the skin to differ from those in the liver.15 Little

is known about the association between the distribution of reactive products

and toxicological effects in various tissues. For example, CYP1A2, which is

the predominant form of the two CYP1Amembers in the liver, is capable of

activating various carcinogens, which give rise to extrahepatic tumors.17

Therefore, it is possible that CYP1A2 represents a case in which activation

occurs in the nontarget tissue and reactive products are transferred to the

target tissue.

Metabolism by CYP enzymes is dependent upon a variety of factors,

including organ, tissue, route and time of administration, individual factors

(e.g., age, gender), multiple concomitant exposures to different agents, and

pathophysiological conditions.16 In addition, genotypic polymorphisms

may influence interindividual differences in chemical activation/deactiva-

tion, which would, thus, contribute to differences in disease susceptibility.

Investigating the roles of various organs and tissues in metabolism and de-

toxification would help understand the interindividual variability in disease

risk with chemical exposure.

2.3. EyesThe eyesmay also be another route of entry for chemicals in the occupational

setting. Typically, ocular exposures occur during handling of chemicals

resulting in accidental splashes or overspray into unprotected eyes. For exam-

ple, agricultural workers are at high risk of ocular exposure to pesticides and

associated ocular toxicity.18 Fortunately, the eye is well protected against

absorption of foreign materials due to the presence of eyelids, tear flow,

and the cornea and conjunctival epithelial barriers of the eye. Furthermore,

although the mammalian eye is seldom considered as an organ of chemical

metabolism, the capacity for phase 1 and phase 2 metabolic activities is pre-

sent.19Most ocular tissues have a rich blood supply, except for the cornea and

lens. The retina receives its blood supply from the retinal artery, which enters

the eyeball along the optic nerve. The outer layer of the retina is adherent to

the choroids, which is a highly vascular tissue that supplies the outer segment

of the retina and the other optic structures with nutrients and oxygen.20 The

blood–ocular barrier regulates the entrance of circulating compounds and

transports metabolic by-products and/or toxicants out of the eye. The three

major sites of transport for polar nutrients into the eye are the uvea, which

consists of the ciliary body and iris, the retina, and the lens.21


Phase 1 metabolic activities are highest in the ocular structures adjacent

to regions of highest uveal blood flow.19 CYP metabolizing enzymes,

monoamine oxidase, cholinesterase, glucuronidase, and aldehyde dehydro-

genase have been found in ocular tissues of mammals, including

humans.22–26 The aldehyde dehydrogenase of the ocular tissues has been

proposed to be involved in ethanol metabolism, lipid peroxidation, and

carbohydrate metabolism.27 Several studies have shown that various

tissues of the eye are also capable of phase 2 conjugation activity; the iris/

ciliary body had the highest GST activity, while the cornea exhibited the

highest activities for NAT and sulfotransferases.28 In addition, ocular

activity for these phase 2 enzymes has been compared to that of the liver

and other extrahepatic organs. The NAT activity in the iris/ciliary body

was nearly as high as that in liver, kidney, or intestine, whereas the GST

activity in the iris/ciliary body was 89% of the activity in the intestine,

and the sulfotransferase activity of the cornea was greater than the same

activity measured in the kidney.19 This supports the hypothesis that the

eye, particularly the cornea, is susceptible to exposure to chemical

compounds and therefore possesses the capacity for chemical biochemical

transformation and elimination.

2.4. Gastrointestinal tractThe gastrointestinal (GI) tract is another important route of entry for

chemicals and is a prominent part of the immune system. The GI tract gen-

erally refers to the stomach and intestine but sometimes to all the structures

from the mouth to the anus. The low pH of the stomach is fatal for most

microorganisms that enter it, and the presence of enzymes, such as CYP3A4,

is important in the detoxification of antigens and chemicals.29 The presence

of gut microflora can produce compounds that induce or inhibit detoxifica-

tion activities. Pathogenic bacteria can produce toxins that enter circulation

and enhance susceptibility to disease combined with exposure to chemicals.

In addition, gut microflora has the ability to remove some conjugation moi-

eties, such as glucuronosyl side chains, converting the chemical to its original

and allowing it to reenter circulation.

The GI tract can be divided into four layers: mucosa, submucosa, muscle

layers, and serosa. The mucosa (innermost layer) is responsible for the

absorption, digestion, and secretion of ingested compounds. Hence, the

mucosa is responsible for the important processes in digestion. The mucosa

is highly specialized in each organ of the GI tract and thus varies structurally


depending on the different functions of these organs. For example, the

mucosa can be folded in order to increase surface area within the small in-

testine, which allows for enhanced absorption of water and nutrients. Com-

promised barrier function of the mucosa will easily allow chemicals to transit

into circulation without opportunity for detoxification. Thus, healthy gut

mucosa is important in reducing toxic load.29

The GI tract is lined with epithelial cells whose cellular membranes are

permeable to small, uncharged (nonionic) solutes. Hence, whether or not a

molecule is ionized will affect its absorption into the circulatory system. The

pH of the environment will determine the proportion of a particular sub-

stance that will be ionized. A weak acid will be in their nonionic form in

the acidic environment of the stomach, while weak bases will be in their

nonionic form in the basic environment of the intestines. Facilitated passive

diffusion and active transport are additional processes that allow compounds

with low lipid solubility (e.g., glucose) to penetrate cell membranes. Follow-

ing absorption in the stomach or small intestine, compounds are taken up

into the liver and undergo first-pass metabolism.

3. QUANTITATIVE APPROACHES IN EXPOSUREASSESSMENT

3.1. Occupational versus environmental exposures
Exposure to chemicals at work is generally greater than that experienced in
environmental settings.30 For example, a farmer’s exposure to pesticides will

be several orders of magnitude greater than residents who are environmen-

tally exposed to pesticides that are emitted from a nearby farm or using pes-

ticides in the home. However, the intensity and duration of environmental

exposures may vary greatly. Residents using pesticides in the home may be

exposed throughout the day compared to a farmer spraying pesticides for

only a couple of hours per day. In addition, the route of exposure may also

be different depending on whether the exposure is occupational or environ-

mental. A farmer applying pesticide may inhale the product and/or have

pesticide deposited on the skin and absorbed into his body. This is in contrast

to environmental or consumer-type exposures in which an individual’s

exposure may occur solely by ingestion of unwashed produce or by transfer

from hand to mouth. Different populations may become exposed to a sub-

stance differently, given the same situation. Children and infants are likely to

experience greater ingestion of pesticide residues than adults due to their

mouthing behavior and greater contact with floor surfaces. While it is


convenient in risk assessment to consider exposed groups in discrete bound-

aries of occupational, environmental, and consumer-type exposure, it is

important to remember that in the real world, exposure is a complex process

influenced by the magnitude, duration, and routes of exposure, as well as a

variety of personal and environmental factors in a number of different

settings.

3.2. Inhalation exposureInhalation exposure has traditionally been the main focus in occupational ex-

posure assessment. Chronic occupational diseases, such as silicosis from expo-

sure to mineral silica, asbestosis and mesothelioma from inhaling asbestos, and

asthma from exposure to diisocyanates, are well recognized and docu-

mented.31–34 Solvents have been shown to cause chronic toxic

encephalopathy, and trichloroethylene exposure has been implicated in

renal cancer in epidemiology studies.35,36 Workers in the polyurethane

industrial sector are exposed to diisocyanates, which are potent lung and

dermal sensitizers and a major cause of work-related asthma worldwide.37

Inhalation exposure is typically measured by drawing a volume of

workplace air using a personal sampling pump and measuring the material

that has deposited on an attached filter or sorbent, which is often placed in

the breathing zone of the worker. The total mass collected is then divided

by the volume of air sampled to obtain a concentration expressed as mg/

m3, ppm, or fibers/ml. A variety of validated sampling and analytical

methods exists for a wide range of chemicals and pollutants, such as

benzene,38 trichloroethylene,39 nitrogen dioxide,40 and sulfur dioxide.41

3.3. Dermal exposureDermal exposure to a variety of chemicals, such as epoxy resins and form-

aldehyde, can lead to irritant or allergic contact dermatitis. Many lipophilic

chemicals can be absorbed readily through unbroken skin and enter the

bloodstream. Dermal exposure and uptake of pesticides and solvents is con-

sidered a significant problemwithin occupational settings. Nonetheless, der-

mal exposure is much less understood compared to inhalation exposure, and

the current methods applied in dermal sampling are nonstandardized and

suffer from methodological issues. Dermal sampling methods can be

grouped into three main categories: surrogate skin, visualization, and

removal techniques. Studies have used surrogate skin techniques, such as

patches or body suites, to collect material that would have been deposited


on the skin surface.42,43 These methods do not measure what fraction will be

absorbed into the skin and hence can overestimate exposure fractions.

Visualization methods use fluorescent tracers to provide information on the

area of skin exposed and, though analysis of fluorescence intensity, the

semiquantitative amount of material deposited.44,45 However, difficulty of

use and their introduction into commercial products has limited their

application in dermal exposure assessment. Removal techniques may

involve wiping or washing materials that remain on the skin surface after

the exposure event.44,46 These methods do not measure what has already

been absorbed into the skin and, hence, can underestimate exposure. Tape

stripping, another removal technique, samples the amount of chemical in

the stratum corneum.47–50 In tape stripping, part of the stratum corneum is

removed, and the rate and extent of dermal absorption may be quantified.

Several occupational exposure studies have demonstrated tape stripping to

be predictive of internal dose, which can be of great benefit in exposure

assessment and epidemiology studies.48,51–53

3.4. Ingestion exposureIngestion exposure to chemicals is not well understood but can be an impor-

tant route. Workers involved in crop harvesting may have considerable in-

gestion exposure to pesticides.54Workers handling products containing lead

(e.g., lead paints, lead batteries) may also experience ingestion exposure to

lead if basic hygiene practices are not followed.55,56 Ingestion exposure is

difficult to assess directly but can be carried out using biological

monitoring when contribution from other routes is well understood.

Data from urine or blood samples can be combined with PBPK models

to enable estimation of how much is absorbed from the ingestion route.57

In addition, ingestion exposure can be assessed using data on surface

residues and observing work practices and activities for hand-to-mouth

and object-to-mouth contact.

3.5. Exposure and dose metricsIn epidemiology, cumulative exposure (CE) is the most commonly used

exposure metric to express the total amount of chemical available to be

absorbed by the subject:

CE¼Xn

1

E� t; ½6:2�


where CE is the cumulative exposure, E is the exposure concentration for a

given job or task, and t is the duration of the exposure. Exposure from each

task is summed across a particular period of time to obtain the CE value for

that subject. This metric, however, does not necessarily indicate the biolog-

ically relevant exposure that can be related to health effects. Checkoway and

Rice suggested that excursions above a certain exposure level (i.e., relative

peak or daily maximum exposure metric) may be better related to certain

disease processes than CE.58 When detoxification occurs by enzyme pro-

cesses that are induced at a certain threshold level, relative peak exposure

metrics may be more closely linked with health effects. Consequently,

selection of an exposure metric should identify the essential factor of the

exposure that causes the health effect (i.e., the metric should be closely

linked with the disease process).30

Exposure assessment does not usually end with the measurement of ex-

posure concentration because that information alone is not useful in

predicting risk of developing adverse health effects unless it is converted

to dose. Biological monitoring plays an important role in providing data

on dose, of which several different types are relevant to exposure estima-

tion (Fig. 6.3).59 The potential (total) dose is the amount of chemical that

could be ingested, inhaled, or deposited on the skin. The applied (contact)

dose is the portion of the potential dose in direct contact with molecules of

the body’s absorption barriers, such as the skin, lung tissue, and GI tract,

and available for absorption. The internal (absorbed) dose, sometimes

called “body burden,” is the portion of the applied dose that is absorbed

into the body through biological membranes (e.g., the lungs, skin, GI

tract) and is therefore available for metabolism, transport, storage, or elim-

ination. The delivered (tissue) dose is the portion of the internal dose that

reaches a tissue of interest. The biologically effective (target) dose is the

portion of the delivered dose that reaches the site(s) of toxic action. PBPK

models predict the portion of the potential dose that is absorbed, delivered

to target tissues, metabolized, and excreted. Thus, biological monitoring

can be used to identify high priority exposures, evaluate the effectiveness

of intervention and prevention strategies, recognize time trends in expo-

sure, establish reference ranges of tissue concentrations, and provide inte-

grated dose measurements.59 Moreover, biological monitoring can be used

to investigate the inter- and intraindividual variability in response to

exposure and, thereby, identify susceptible individuals/groups for further

evaluation of health risks.

Emission Source

Pathways

Exposure Concentration

Applied Dose

Internal Dose

Biologically Effective Dose

Biological Effect(s)

BiologicalMonitoring

Intake

External

Internal

Absorption Barriers

(skin, lung tissue, GI tract)Uptake

Adverse Effect(s)

Delivered Dose

Potential Dose

Figure 6.3 Dose types relevant to biomonitoring within exposure assessmentframework (modified from Pirkle et al.59).


4. INTEGRATING BIOLOGICAL MONITORING ANDTOXICOLOGY INTO RISK ASSESSMENT

4.1. Biomarker selection
Biological monitoring is not a new phenomenon. For more than a century,
physicians and industrial hygienists have monitored body fluids in worker

populations for exposure to a variety of hazardous substances. Clinical med-

icine provides historical and contemporary lessons on the value of measuring

body fluids for indicators of adverse health risk.9 The expanding availability

of biomarkers in exposure assessment and epidemiology offers increasing

potential to apply biomarker data to evaluate exposure, quantify dose,

and predict health effects in exposed populations. The absorbed doses for

hundreds of chemicals can be measured by looking for biomarkers of


exposure in human tissues and fluids, including saliva, blood, urine, hair,

feces, breast milk, and fingernails. For unchanged chemicals (i.e., parent

compounds), such as heavy metals, polychlorinated biphenyls (PCBs),

and solvents, the biomarker measured is the chemical absorbed into the

body. For chemicals that are metabolized/biotransformed in the body,

the biomarker measured is the metabolite of the chemical (e.g., phenol as

a biomarker for benzene). In addition, endogenously produced molecules

can be used as exposure markers for some compounds (e.g.,

acetylcholinesterase enzyme activity can be the biomarker for uptake of

organophosphate pesticide). Biomarkers of effect can also include disease

markers, molecular changes, and cellular/tissue changes (e.g., DNA adducts,

protein adducts, and sperm count can reflect the effects of some chemical).

Genotypes responsible for interindividual differences in enzymes involved in

bioactivation and detoxification of chemicals are recognized as biomarkers

of susceptibility to toxicity. Table 6.2 summarizes common biological

matrices and corresponding chemicals that have been sampled and analyzed

in occupational exposure studies.

Table 6.2 Biological matrix and chemicals analyzed in occupational exposureassessment studiesBiologicalmatrix

Chemicals(examples)

Type ofsamples Collection Issues References

Blood Low MW

compounds,

gases, metals,

pesticides,

semivolatile

organics,

VOCs,

diisocyanates

Whole

blood, red

blood cells,

white blood

cells, plasma,

protein

Invasive,

quick,

requires

trained

phlebotomist

Plasma can be

analyzed for

lipophilic

substances

60–62

Breath Gases,

semivolatile

organics,

volatile

organics

Mixed air,

alveolar air

Noninvasive,

quick,

requires air

sampling

apparatus

Represents

recent

exposure

63

Hair Cotinine,

metals, PCBs

Scalp Noninvasive,

standardized

collection

location

High potential

for external

contamination

64–66

Table 6.2 Biological matrix and chemicals analyzed in occupational exposureassessment studies—cont'dBiologicalmatrix

Chemicals(examples)

Type ofsamples Collection Issues References

Saliva Metals,

pesticides,

semivolatile

organics

Mixed saliva Noninvasive,

easy for

participant

Potential for

contamination

from materials

in mouth

67,68

Skin Volatile

organics,

pesticides,

diisocyanates

Top skin

layer

(stratum

corneum)

Tape

stripping,

noninvasive

Measures

absorption into

skin, can

standardize by

keratin content

48–50

Urine Low MW

compounds,

metals,

mutagens,

pesticides,

semivolatile

organics,

diisocyanates

Spot or grab

samples, first

morning

void, 24-h

urine

samples, total

urine void

Noninvasive May have to

standardize to

creatinine

concentration

or specific

gravity

48,53,69,70

PAHs, polycyclic aromatic hydrocarbons; PCBs, polychlorinated biphenyls; PBDE, polybrominateddiphenyl ethers.


Chemicals and/or their metabolites that are readily absorbed through bi-

ological membranes are distributed through blood vessel networks within the

body. Hence, blood is one of the most frequently used matrices to measure

biomarkers of exposure and calculate dose. Additionally, many relevant bio-

markers of effect, which include early biological response as well as clinical

effects, can be measured in blood. For example, widespread perturbation of

gene expression following chronic benzene exposure among workers was ob-

served, indicating gene expression biomarkers of early effects.71 Therefore,

blood biomarkers can provide the apparent link between exposure, dose,

and health effect. Abundant blood proteins, such as albumin and hemoglobin,

can be isolated andmeasured for protein adducts.72,73 Because the lifespan of a

red blood cell is 90–120 days, and the half-life of albumin is 20 days, protein

adducts may reflect past exposures to chemicals. However, collecting blood is

invasive and many oppose having their blood drawn. Blood sample volume is

relatively small, which may limit detectability of biomarkers and the number


of assays performed. Additionally, blood concentrations may not be accurate

measurements of the total body burden of lipophilic compounds, such as

heavy metals, which concentrate in fatty tissues and tend to have long

biological half-lives. Therefore, knowledge on the magnitude and timing

of exposure and the distribution/elimination kinetics of these compounds

in the body is important in interpreting and utilizing blood biomarker data

in risk assessment.

Urine is often the preferred sample for collection in biological monitoring

programs.Urine collection is noninvasive, and the sample volumes can be large,

which facilitates sample collection and analysis relative to blood. However, for

many chemicals, it may not be a reliable indicator of exposure because it often

contains excreted metabolites instead of parent compounds. Lipophobic (i.e.,

hydrophilic) compounds have relatively short biological half-lives and tend

tometabolize rapidly. Theirmetabolites are evenmore lipophobic than the par-

ent compounds and are excreted in the urine. Consequently, assessment of ex-

posure to nonpersistent compounds is generally conducted using urinary

biomarkers, and concentrations are reported as the amount/volume in urine

or on an adjusted basis using creatinine, specific gravity, or osmolality.74 How-

ever, interindividual differences in phase 1 or 2 metabolism of chemicals due to

genetic polymorphisms can complicate exposure–dose and dose–response

associations. For example, there has been increased attention focused on the role

of genetic factors in modifying individual susceptibility to asthma. Polymor-

phisms in GST or NAT genes may result in interindividual variation in GST

or NAT enzyme activities that are involved in 1,6-hexamethylene diisocyanate

metabolism, and could lead to an increased risk of asthma development.75

Workers possessing the slow N-acetylator phenotype may excrete less acety-

lated metabolites than those with the fast N-acetylator phenotype and, thus, fa-

vor oxidation pathways that may increase the risk of disease development.76

Thus, the types and relative amounts of metabolites formed as well as suscep-

tibility of disease are partially driven by the worker’s genetic makeup.

The timing of sample collection requires detailed knowledge of

toxicokinetics of the compound. Some compounds have both short- and

long-term elimination kinetics (i.e., biphasic elimination) due to the variable

excretion of formed protein adducts and unbound metabolites, which can

also influence exposure–dose relationships.53 Urine tests, such as urine

osmolality, protein level, and creatinine clearance, can be used to assess

kidney function, which can adversely affect chemical metabolism and

clearance. Lastly, urine collection is often employed in large-scale exposure

assessment and epidemiology studies due to ease of sample collection.


Selection of the appropriate tissue or fluid for biological monitoring is

based on the properties of the particular compound of interest and, in some

cases, the time interval since the last exposure.9 For example, certain lipophilic

chemicals, including dioxins, PCBs, and organochlorine pesticides, have long

biological residence times in the body (months or years), whereas lipophobic

chemicals, such as organophosphate pesticides and volatile organic com-

pounds, have relatively short biological residence times (hours or days) and

undergo rapid metabolism and excretion in the urine. Therefore, lipophilic

chemicals are typically measured in blood and lipophobic chemicals are mea-

sured in urine. The time since the last exposure and toxicokinetics of the com-

pound can also play a role in determining the best biological specimen for

analysis. For example, benzene, a nonpersistent compound, is excreted rela-

tively rapidly (within hours), and urine is collected immediately following

exposure.77 Therefore, urine or blood collection should be tailored according

to the exposure pattern and physical–chemical properties of the compound.

4.2. Biomarker validationValidation of biomarkers of exposure, effects, and susceptibility occurs at

three levels: (1) sample collection and stability, (2) sensitivity and reproduc-

ibility of analytical method, and (3) sensitivity and specificity of the biolog-

ical marker itself to the exposure or health effects. For biomarkers of

exposure, the biological marker must demonstrate that an exposure is occur-

ring or has occurred, and can be used to separate individuals on the basis of

their level of exposure. The temporal relevance of the marker, identifying

background variability, and determining potential confounding factors are

important components of biomarker validation. Temporal relevance is crit-

ical since it relates the timing of exposure to the appearance of a measurable

biomarker and to the duration of time that the biomarker is measurable fol-

lowing cessation of exposure. Validation of a biomarker occurs in the lab-

oratory, pilot, and ultimately in population studies, which will demonstrate

application of the biomarker in environments that most closely resemble the

population exposure settings under investigation.

For occupational exposure assessment, compound-specific markers of

exposure, such as blood lead concentrations,maybe thepreferred choice.How-

ever, compound-class-specific markers (e.g., PAH–DNA adducts) or non-

specific markers (e.g., acetylcholinesterase inhibition) may be available.

Dialkylphosphatebiomarkers (i.e., dimethylphosphate, dimethylthiophosphate,

dimethyldithiophosphate, diethylphosphate, diethylthiophosphate, and


diethyldithiophosphate) are compound-class-specific markers of organophos-

phorus pesticide (OP) exposure. For example, dimethylphosphate is the bio-

marker for malathion, dichlorvos, and mevinphos, exposure, while

diethylphosphate is the biomarker for thion, terbufos, and diazinon exposure.78

Different OPs can cause different health effects; some cause developmental or

reproductive harm, some are carcinogenic, and others are known or suspected

endocrine disruptors. Therefore, association between biomarkers of OP expo-

sure and health risksmay require some knowledge of the exposure environment

andwhich pesticides are being used.Nonspecificmarkers of contaminants (e.g.,

non-chemical-specificbiological changes) as indicatorsofexposuremaybemore

sensitive than externalmeasures of exposure to predict an individual’s total dose.

Acetylcholinesterase inhibition is widely regarded as a good biomarker of

exposure to OPs. However, acetylcholinesterase activity is also inhibited by

metals, organochlorines, herbicides, and surfactants, which has led to questions

regarding the use of acetylcholinesterase inhibition as a specific marker of OP

exposure.79 Because nonspecific markers are neither source- nor chemical-

specific, characterizationof thevariabilityof thesemarkers inhumanpopulations

is an important validation component prior to use in exposure assessment.

The complexity of exposures from occupational and/or environmental

sources and interindividual variability in response to exposure can result in ad-

ditive, multiplicative, or antagonistic effects on health end points. For exam-

ple, smoking status and occupations with known risk for bladder cancer

demonstrated a multiplicative effect between these two factors on risk of uri-

nary tract cancer.80Conversely,NATpolymorphismmay be a protective bio-

marker of susceptibility, indicating an antagonistic effect for developing

disease; subjects with the NAT1 allele 10 had a significantly reduced risk of

developing bladder cancer.81 Greater than additive joint effects were observed

for the presence of the Ala/Ala genotype (i.e., polymorphism in the manga-

nese superoxide dismutase gene, Ala-9Val), smoking, radiation to the chest,

and occupational exposure to ionizing radiation on breast cancer develop-

ment, while antagonism was observed between the Ala/Ala genotype and

the use of nonsteroidal anti-inflammatory drugs.82 Such complex interactions

between various exposure scenarios and individual susceptibility markers on

disease development require multiple phases of biomarker validation, ranging

from establishing analytical reliability (e.g., sensitivity and specificity) of the

biomarker to evaluating the relationship between external variables (i.e.,

chemical exposure) on the association between the biomarker and health out-

come and to investigating the associations between exposure, biomarker, and

health outcome in population studies.


Ideally, biological monitoring data from environmental or occupational

settings are supported by quality control, analytical standardization, avail-

ability of control groups, and other mechanisms to limit uncertainty and var-

iability.5 Biological monitoring programs for assessing exposures to

environmental chemicals generally require that the measurements of the rel-

evant analytes are at much lower concentrations than needed in clinical or

animal toxicology studies, thus posing considerable analytical challenges.

Therefore, biological monitoring approaches for assessing exposures to en-

vironmental chemicals must employ analytical methods with high sensitivity

and specificity to limit the uncertainty for measuring low-level concentra-

tions. Analytical methods used for quantifying exposure and biomarker

levels should also demonstrate reproducibility, particularly at the low levels

of exposure. Improved analytical capabilities make possible the accurate and

precise measurement of many chemicals at low levels in the environment

and in human specimens, demonstrating human exposure to and absorption

of chemicals, and often their ADME in exposed populations.5

4.3. Exposure modelingDirect and indirect exposure measurements involve several important issues:

measurements may be expensive and difficult to obtain, provide only a single

snapshot of exposure at a given time, and vary greatly between individuals in

the same exposure setting. Measurements will vary between people in the

same environment (interindividual variation) and for the same person over

time (intraindividual variation). Therefore, exposure assessment should at-

tempt to assess the variability in exposure between people and across a period

of time through repeated sampling. The ideal measure of exposure (e.g.,

breathing-zone concentration) should accurately reflect the dose or biolog-

ically relevant amount of material absorbed into the body. Exposure model-

ing can address these issues to some extent by providing a mechanism to

mathematically characterize exposure variability and a means to evaluate

exposure when data measurements are sparse or absent.

Exposure models can be divided into three broad categories: statistical,

deterministic, or probabilistic models. Statistical (e.g., regression) models are

generally numerical best fits between collected exposure measurements and

potentially related factors (e.g., personal protective equipment (PPE)). Such

models cannot be considered reliable for predicting exposures outside the

original study population and environmental setting without validating

them for that specific purpose. Deterministic (or physical) models are based


on logical constructs of the physical environment and human behavior. Such

models need to be validated using exposure data and can in principle be used

for exposure prediction in new populations and environmental settings. De-

terministic assessments use point assessments of specific variables to generate

a single estimate of exposure and risk based on various assumptions about the

exposure scenario. Deterministic assessments often begin with the worst-

case assumptions (e.g., maximum application rate of pesticide), then can

be refined by more realistic values that remain point estimates (e.g., average

application rate per day).

For short-term or acute operator exposure assessments, regulatory pro-

cedures require the assessment to address maximum permitted application

parameters. Thus, each deterministic assessment provides a single value

estimate for exposure regardless of the input information. Therefore, the

advantages of the deterministic approach are that it is easy to conduct com-

pared to probabilistic assessment and that input variables do not require ex-

tensive supporting databases. In addition, a single risk estimate is easy to

understand, and the risk assessment process has been developed around

the deterministic approach to risk assessment, which is already well under-

stood among industry groups and regulators. The primary disadvantage of

the deterministic approach to exposure and risk assessment is that large

amounts of knowledge regarding patterns and exposure potential are not in-

corporated into exposure assessment. In addition, the variability and uncer-

tainty that surrounds the point estimate of exposure are not reflected, and

there is hidden compounding of conservatism built into the point estimate.83

In contrast, probabilistic exposure models incorporate the measured or

estimated distributions of the input variables (e.g., dermal and inhalation

exposure levels), and thus, they produce more realistic population exposure

distributions than deterministic models. In probabilistic risk assessment, one

or more variables in the risk equation are defined as a probability distribution

rather than a single number. Regulatory decisions regarding operator or

consumer exposure to pesticide products have traditionally been based on

deterministic exposure assessments. However, with the development of

computer modeling and exposure databases, probabilistic assessments, some-

times referred to as Monte Carlo analysis because of the underlying use of

probabilities, are being developed more frequently and utilized in regulatory

processes.

The most common numerical technique for probabilistic risk assessment

is Monte Carlo simulation. All probabilistic assessments utilize the distribu-

tion of possible occurrences for the input variables in the exposure or risk


assessment algorithm. Based on the statistical description of the distribution

of each variable, the probability program will randomly select a discreet rep-

resentative point estimate for that variable. When the same algorithm is cal-

culated repeatedly, the distribution of predicted outputs begins to represent

the range of possible outcomes for the exposure assessment of interest.

Monte Carlo simulation integrates varying assumptions, usually about expo-

sure, to come up with possible distribution (or ranges) of risk. Therefore,

probability analysis uses all the information available to provide a more

accurate and detailed assessment of possible exposure outcomes instead of

a single point deterministic assessment. The main advantage of the probabi-

listic approach is that it can provide a quantitative description of the degree

of variability or uncertainty (or both) in risk estimates. The quantitative anal-

ysis of variability and uncertainty can provide a more comprehensive char-

acterization of risk than is possible with the deterministic approach. Another

important advantage of the probabilistic approach is the additional informa-

tion and potential flexibility it affords the risk manager. The risk manager can

select a specific upper-bound level from the high-end range of percentiles of

risk, rather than high-end point values. The primary disadvantages of the

probabilistic approach are that generally it requires more time, resources,

and expertise on the part of the assessor, reviewer, and risk manager than

a deterministic approach. Also, the distribution of the likely outcomes un-

covers the higher end of the exposure distribution, which can receive too

much focus while having small chances of occurrence.83

Probabilistic risk assessment is an important tool in risk management strat-

egies. For example, a non-dietary exposure probabilistic model was developed

for regulatory purposes regarding pesticide products, based on the use of

databases available to assess operator or residential non-dietary exposure.83

Here, the goal of the probabilistic assessment was to characterize the variability

and uncertainty in the exposure and risk assessment. In the process, the vari-

ables that impart the greatest influence on determining exposure can be

determined, which can become the focal points of exposure mitigation strat-

egies that are identified by the risk assessment.While these assessments utilize a

greater amount of data available to assess exposure, the complexity of the

exposure and risk assessment is increased. Therefore, guiding principles

among regulatory agencies, industry, and affected populations are needed

to assess exposures and interpret the outputs of these assessments.

Many process or scenario-specific exposure models exist to predict

exposures among populations.30 The U.S. Environmental Protection

Agency (EPA) has developed a variety of exposure assessment tools and


models to help evaluate what happens to chemicals when they are used and

released into the environment and how workers, the general public, con-

sumers, and aquatic ecosystems may be exposed to chemicals.84 In regard

to occupational exposure, the Wall Paint Exposure Assessment Model

helps predict the likely level of a worker’s exposure to chemicals during

brush or roll painting activities. Among the other tools available is the

Chemical Screening Tool for Exposures and Environmental Releases.

The system allows users to enter data about a particular product and typical

use scenarios to estimate occupational inhalation and dermal exposures to a

chemical during industrial and commercial manufacturing, processing, and

use operations involving the chemical. Another tool is the Use Clusters

Scoring System, which screens clusters of chemicals that are used in a par-

ticular work task. The system uses health risk or toxicity scores for each

chemical to calculate an overall cluster score, which is an indicator of po-

tential risk for the cluster.

4.4. Exposure–dose modelingA fundamental issue in the quantitative aspect of exposure assessment is the

characterization of interindividual variability in exposure. The pattern of ex-

posure may differ within individuals, groups, or populations. For example,

workers in the same factory may have different exposures as a result of dif-

fering work habits.85 Therefore, observational schemes should not be relied

upon by investigators to guarantee that groups of workers are uniformly ex-

posed. Biomarkers represent one strategy to assess interindividual variability

in exposures whenmeasured environmental concentrations do not differ be-

tween individuals. Genotype, dietary habits, body mass index, state of

health, lifestyle habits (e.g., smoking), and behavior may all play a role in

determining an individual’s exposure. Predictive models describing the

relationship between chemical exposure and biomarkers can be used to

identify the major routes of exposure (i.e., dermal, inhalation, ingestion),

and workplace and individual-level covariates that modify these relation-

ships (e.g., engineering controls, PPE use). These models may also be used

to investigate interindividual variability in metabolism to better understand

susceptibility to disease, such as NAT or GST gene polymorphisms.

Linear regression models have been used to demonstrate relationships

between occupational exposure to chemicals and biomarker levels in blood

and urine,52,53,60,73,86 indicating the utility of these biomarkers in exposure

assessment. Logistic regression, or generalized linear modeling, has also been


used to investigate associations between biomarker levels and PPE use, which

is important in evaluating intervention and prevention strategies.86,87 Linear

mixed effectsmodeling can be implemented to identify themain determinants

of exposure levels88,89 and biomarker levels52,86 where repeated measures

have been performed. Mixed models are statistical models that incorporate

both fixed and random effects in making predictions of a dependent

variable (e.g., biomarker concentration). The models are useful in settings

where repeated measures are made on the same statistical unit (e.g.,

worker) over time. Due to the likelihood of serial correlation with

repeated measures, the mixed model approach would be appropriate

method for biological samples, where biomarkers may persist for extended

periods.

The general form of the linear mixed model investigating the association

of exposure concentration, as well as workplace covariates, and biomarker

concentration is

Yij ¼ b0þb1X1ijþb2X2ijþaiþ eij; ½6:3�where Yij represents the log-transformed biomarker concentration (the jth

measurement obtained for the ith worker), X1ij represents the log-

transformed exposure concentration (e.g., inhalation exposure), X2ij

represents the covariate being tested (e.g., respirator use), b1 and b2 representregression coefficients of the explanatory variables, b0 represents the

intercept, and ai and eij represent the random effects associated with the

worker (ai for i¼1, 2, . . ., n worker) and an error term (eij for j¼1, 2,

. . ., N measurement for the ith worker). The between- and within-worker

variance can be expressed as s2b and s2w, respectively, and the total variance

is s2Y¼s2bþs2w. It is assumed that Yij is normally distributed with mean

my¼b0þb1X1ijþb2X2ij and variance s2Y.These statistical models have been used to evaluate the association be-

tween exposure and biomarker concentrations, and how personal and work-

place covariates, such as PPE use, modify these associations. For example,

protective and engineering controls, such as coverall use and ventilated

booth type, have been shown to affect blood and urine concentration levels

of 1,6-hexanediamine, a biomarker of 1,6-hexamethylene diisocyanate

exposure among painters in the auto-repair industry 52,86. Identifying

sources of the between- and within-worker variability in biomarker

measurement would be important in investigating prevention and

intervention strategies to reduce exposures and help validate biomarkers

for use in exposure assessment.


4.5. Toxicokinetics and toxicodynamicsToxicokinetics describes the ADME of an exogenous compound, while

toxicodynamics describes the actions and interactions of the compound

within an organism or tissue. Understanding the toxicokinetics and

toxicodynamics of an agent is critical for the development and use of bio-

markers of exposure and investigation of their relationship with exposure

and health effects. Characterizing the ADME of exogenous compounds is

important for both the understanding and application of the mode of action

in predicting toxicity. Differences in toxicokinetics and toxicodynamics across

species, individuals, and exposure patterns (routes, level, duration, frequency)

can lead to different biological effects for the same total exposure to a chem-

ical. Thus, information on toxicokinetics and toxicodynamics can be used to

establish the relationship between degree of exposure and related biomarkers

to determine allowable levels for chemical exposure (e.g., Biological Exposure

Indices (BEI)90 or Biological Tolerance Values (BAT)91), to establish proper

sampling time of biological samples, and to identify sources of biological

variability in response to exposure.92 The biological half-life of the chemical

and timing of sampling relative to the exposure are important factors in de-

termining the utility of biomarkers for assessing exposure. Differing elimina-

tion kinetics determine whether the biomarker reflects recent exposure,

historical exposure, or an integrated measure of exposure over time (i.e., CE).

Traditional risk assessment is generally based on external exposures. The

administered dose, identified as the no-adverse-effect level or lowest-

observed-adverse-effect level, in animal toxicity studies, is incorporated

with uncertainty factors or adjustments to determine a safe exposure level

for humans. The estimated daily exposures in human populations are then

compared with the health-based criteria for acceptable, safe, or tolerable

daily intakes (e.g., Reference Doses (RfDs), Tolerable Daily Intakes, or

Minimal Risk Levels) to evaluate whether exposures exceed such criteria.

Alternatively, biological monitoring data is a measure of internal exposure

and, thus, has the potential to provide a more biologically relevant measure

of true exposure, that is, provide estimates that are more directly related to

the concentration of the active chemical at the target site or organ, a key

determinant of toxicity and biological response than external exposure mea-

sures. To be put in a risk context, the integration of biological monitoring

data, human exposure data, and animal toxicity data needs to be performed.

Three approaches exist for linking biological monitoring data to health

risks: direct comparison to toxicity values, forward dosimetry, and reverse

Reverse dosimetry

(Administered dose intoxicity studies)

PBPK ModelingHuman blood/tissue chemicalconcentration(biological monitoring studies)

Animal blood/tissue chemicalconcentration(toxicity studies)

PBPK Modeling

Forward dosimetry

Human exposures

Animal exposures

Traditional

Risk AssessmentMargin of Safety

(Chemical concentrations inthe environment)

Figure 6.4 Relationship between human biomonitoring data, animal toxicity data, andhuman exposure data for application in risk assessment (modified from Tan et al.93).


dosimetry using PBPK modeling (Fig. 6.4).93 PBPK models are useful in

that they support the low-dose and interspecies extrapolations from animal

toxicity studies to humans that are important components of current risk

assessment methodologies.94 Despite its well-accepted role in improving

the scientific basis of risk assessment procedures, PBPK modeling has been

applied only recently in estimating human exposure distributions or health

risks using biological monitoring data.93,95–97

Biological monitoring data can be directly compared to toxicity values in

cases where the relationship of the biomarker to health effect of concern has

been characterized in the humans. In forward dosimetry, PBPK data from

experimental studies and human exposure data can be used to estimate

the chemical concentration in target tissues, providing an estimate of the

margin of safety in humans. In one example of forward dosimetry, a

dermatotoxicokinetic (DTK) model was developed to predict the ADME

of aliphatic and aromatic components of jet fuel (JP-8) following dermal

exposure in humans.51 Exposure is a concern among workers in the military

and commercial airline industry, who are exposed to JP-8 through inhala-

tion and dermal contact. Kinetic data were obtained from 10 volunteers fol-

lowing a single dose to JP-8, and the measured biomarkers in the blood were

used to develop the DTK model. The model was developed to better

understand the relationship between exposure to JP-8 components in the

skin and the internal dose received and further to be used for the develop-

ment of strategies to investigate multiroute exposures. Investigating multiple

exposure pathways, which are typical in occupational exposure scenarios, is

necessary to accurately predict doses to target tissues and target tissue effects

thereby improving risk assessment.

Inhalation

Lung airspace

Lung blood

Fat

Skin

Rapidly perfused tissue

Slowly perfused tissue

Kidney

Venous blood

Liver

Metabolism

Wateringestion

Dermalexposure

Figure 6.5 Schematic of the PBPK model for chloroform, bromodichloromethane,dibromochloromethane, and bromoform.93 (Reprinted with permission fromMacmillanPublishers Ltd: Journal of Exposure Science and Environmental Epidemiology 17:591–603, copyright 2007.)


In contrast, reverse dosimetry uses PBPK data from experimental studies

and biomonitoring data to estimate a distribution of exposure levels for

comparison with an animal-based health standard, such as an RfD or

Reference Concentration. In one example of reverse dosimetry, a PBPK

model for trihalomethanes (THMs), consisting of chloroform,

bromodichloromethane, dibromochloromethane, and bromoform, was de-

veloped (Fig. 6.5) and used, in combination with Monte Carlo simulation

and probabilistic information, to generate a distribution of trihalomethane

exposures consistent with human biomonitoring data.93 The estimated dis-

tribution of exposure can be compared with regulatory guidelines (e.g.,


maximum contaminant level), established using animal toxicity data, to

estimate the proportion of the population above levels associated with health

risk. Because trihalomethane exposure can occur through inhalation, inges-

tion, and dermal contact, the PBPK model should examine multiroute

exposure scenarios. Another important factor in this study was to accurately

characterize metabolism of THMs using biological monitoring data when

estimating relevant health risks. This is because the toxic substances are

metabolites of THMs, not the parent compounds. Therefore, identifying

the extent of metabolic inhibition may be important for interpreting

biomarker concentrations and predicting health risks.

5. EMERGING ISSUES AND TECHNOLOGIES

5.1. Variability and uncertainty
Biological monitoring data can be used inmultiple ways (e.g., exposure assess-
ment, to investigate metabolic pathways). In terms of risk assessment and risk

management, biological monitoring has the potential to be a valuable tool.

Given the increasing sensitivity of analytical methods, detection of a chemical

in biological samples, such as blood or urine, should not be confused with or

equated to increased risk. Exposure information and statistical associations be-

tween exposure and dose must be evaluated against relevant toxicology data

and human epidemiology data to imply causation between exposure and

health effects.5 With enhanced methods for detection and collection of large

exposure data sets, issues related to variability and uncertainty become impor-

tant considerations in risk assessment and risk management. In the context of

exposure assessment, variability may refer to measurable differences in expo-

sure between individuals in a population, which can be assessed through sam-

pling methods and quantitative analyses. Uncertainty arises from one’s lack of

complete knowledge, and it may be related to a model used to characterize

risk, the parameters used to provide values for the model, or both. Therefore,

uncertainty may result in making a nonoptimal choice because one may ex-

pect one outcome but a different outcome might actually occur.

An increased consideration of variability and uncertainty in risk assess-

ment, combined with significant advancements in computational speed

and capability, has motivated a greater shift toward probabilistic risk assess-

ment.98 Sensitivity analysis is one method to address uncertainty by chang-

ing one uncertain input at a time and showing how the results of a model

change across the range of possible values. As the number of inputs allowed

to vary increases, the sensitivity analysis transitions into a probabilistic


uncertainty analysis (typically usingMonte Carlo simulation methods). Such

methods are improvements over past practices of using point estimates of

risks and benefits since any point estimate most likely represents a gross sim-

plification that ignores important underlying dynamics.98 Therefore, this

quantitative analysis can provide a more comprehensive characterization

of risk than is possible in the point estimate approach. Because assessment

techniques are often based on qualitative or experience measures, and

chronic and potential cancer risks of chemicals lack clearly defined quanti-

tative assessment methods, difficulties arise in assessing risk scientifically and,

consequently, in delivering prompt solutions to occupational risk assess-

ment.99 The primary advantage of a probabilistic risk assessment is that it

can provide a quantitative description of the degree of variability or uncer-

tainty (or both) in risk estimates for both cancer and chronic health effects

and thus ensuring a scientific basis to control occupational hazards. How-

ever, much work still needs to be done to develop effective ways to present

the results of probabilistic risk assessments and sensitivity analyses to risk

managers and to the public to ensure that the results ultimately lead to

improved risk management decisions.

5.2. Gene–environment interactionToxicology has been relied upon as the foundation to assess risks to human

populations from environmental and occupational factors. New advances in

systems biology and technologies derived from genomic research have created

exciting possibilities for application in human health risk assessment.

Toxicogenomics is the application of genomic technologies (e.g., trans-

criptomics, proteomics, metabolomics, genome sequence analysis) to study

the effects of environmental chemicals on human health and the environment.

While biological research is considered primarily reductionist in its history

(i.e., hypothesis-based research in what makes up biological systems/organs),

systems biology is viewed as holistic in seeking to discern interactions between

components of a biological system and integrating these interactions into

networks/pathways using rigorous mathematical models.100 Therefore, its

ultimate goal is to understand the dynamic networks of regulation and inter-

actions (e.g., metabolism, cell signaling, DNA repair) that allow cells and or-

ganisms to live in a highly interactive environment and to understand how

perturbations in the system (e.g., chemical exposure) cause disease.100 With

this information, interindividual differences in how chemicals interact with

biological systems to cause disease can be determined.


Interindividual differences in how various toxicants are metabolized or in

DNArepair capacity and efficiency based on gene polymorphism,whose source

may include single nucleotide polymorphisms (SNPs) or copy number varia-

tions, may influence susceptibility to disease (i.e., gene–environment interac-

tion). Polymorphisms in genes regulating enzymatic metabolism may result in

higher levels of activemetabolites,which, in turn, can lead to increased reactions

with DNA and macromolecules and susceptibility for disease. Various occupa-

tional studieshave linkedgenotypepolymorphismson levelsof chemicals and/or

their metabolites in biological samples,101–103 early biological indicators of

genotoxicity (e.g., chromosomal damage and micronucleated binucleated

cells),101,104,105 and disease risk from occupational chemical exposure.21,106

For example, candidate gene and genomewide association analyses were

performed to investigate individual differences in SNPs as genetic markers

associated with naphthyl–keratin adduct levels measured in the skin of fuel-

cell maintenance workers exposed to naphthalene.103 The relative

contribution of these SNPs, as well as personal and workplace factors, on

naphthyl–keratin adduct levels was determined using a multivariate linear

regression model. Thus, advances in genotyping strategies (e.g., DNA

microarrays), exposure assessment studies utilizing biological monitoring, and

computational methods to identify candidate significant polymorphisms (e.g.,

using candidate gene and genomewide association analyses) can improve

predictive models linking exposure and disease.103

Genomic data have the potential to inform toxicodynamics,

toxicokinetics, inter- and intraspecies differences in toxicodynamics and

toxicokinetics, modes of action, and dose–response assessment. Inclusion

of individual genetic differences into exposure models will aid in the predic-

tion of adverse health effects to a susceptible population. Epigenetic markers

that regulate gene expression, such as DNA methylation or histone modi-

fication, may be affected by CE to chemicals. For example, DNA methyl-

ation was inversely associated with lead exposure, which may have

important implications for the mechanisms of action of lead on health out-

comes.107 In mines and refineries, workers become exposed to harmful par-

ticulate or soluble metal ions, which has been associated with increased

susceptibility to cancer. Exposure to ambient air particulate matter (PM)

containing metal-rich particles was associated with peripheral blood leuko-

cyte DNA methylation in tumor suppressor genes among steel workers,

suggesting that methylation alterations may reflect processes related to

PM-induced lung carcinogenesis.108 Exposure to insoluble nickel com-

pounds may play a role in the development of lung and nasal cancers.


The formation of nickel ions in cells induces the production of reactive

oxygen species and may repress tumor suppressor gene expression through

histone modification.109 Thus, research on the contribution of genetic dif-

ferences in metabolism and disease development should lead to the devel-

opment of more sophisticated predictive models and improved protection

strategies for exposure and risk assessment.

Investigating gene–environment interaction with chemical exposures

poses unique challenges in case–control studies. In general, case–control stud-

ies are the most feasible study design for investigating occupational exposures

and rare diseases. However, exposure data of many specific agents in the study

population may be lacking, and workplace records to reconstruct exposures

accurately may be difficult to obtain. A comparison of two different methods

of occupational exposure assessment for lead in a case–control study of adult

brain tumors concluded that high-quality exposure data are needed to detect

gene–environment interactions.110 In that study, effect modification of the

association between expert-assessed lead exposure and risk of brain tumors

was perceived by a single SNP in the ALAD gene.110 However, when a less

intensive method of exposure assessment (i.e., job exposure matrix) was

employed, no such evidence for effect modification by genotype was found.

The results indicated that investigators would benefit from using the most

accurate method of exposure assessment available. The attenuating effects

of exposure misclassification would result in increased sample size require-

ments to detect effectmodification, whichwould offset any savings from using

a less costly method of exposure assessment. Thus, high-quality exposure data

are needed to improve the ability to detect genetic effect modification.

These rapid advances in systems biology and genomics can be over-

shadowed by several challenges, including inconsistencies in study design

and sampling strategies, lack of quantitative or qualitative correlations of

exposure, dose or adverse health effects, and the lack of bioinformatics

and analytical tools to manage the volume of research findings.111 Much

work needs to be done determining how these advances in systems biology

and genomic research may be integrated and applied for improving toxicol-

ogy, epidemiology, risk assessment, and the protection of human health.

Currently, the EPA provides no set strategies for incorporating genomic data

into risk assessments of environmental agents. However, the EPA has con-

ducted an evaluation of the potential impact of genetic polymorphisms in

key metabolizing enzymes on the variability in enzyme function across eth-

nically diverse populations.112 In addition, efforts are being made to enhance

interdisciplinary collaboration that will foster the integration of genomics


into exposure and risk assessment strategies. The Developmental Toxicol-

ogy Exploratory Research program, sponsored jointly by the National

Institute of Environmental Health Sciences and the American Chemistry

Council’s Long-Range Research Initiative, was formed to stimulate the in-

teraction and collaboration of developmental toxicologists, developmental

biologists, and geneticists to integrate these new research directions and ex-

pand knowledge on the mode of action of developmental toxicants. The

mission of the program is to improve interdisciplinary advances in toxicol-

ogy by better linking databases of developmental toxicology, developmental

biology, and genomics and to establish multidisciplinary outreach programs

for the effective exchange of information and techniques related to the

assessment of toxicity.113

5.3. In vitro models in risk assessmentTraditionally, much of the hazard data on chemicals have been generated by

applying technologies developed for histological and clinical chemical analyses

to animal models. Today, improved cell- and/or tissue-based (human and

animal) in vitromodels are becoming increasingly available, and new technol-

ogies, such as proteomics and bioinformatics approaches, have generated and

interpreted new types of nonanimal data.114 However, developing ways to

integrate these data into making risk-based decisions represents a major chal-

lenge. The new technologies being developed (i.e., “omics” technologies, in-

formatics, advanced analytical methods, and bioengineering) could enhance

the scientific basis of public health protection and enable the transition away

from animal testing.114 Human tissue models are now used routinely in the

pharmaceutical industry to predict human metabolism, select appropriate

animal models, and identify potential drug–drug interactions. Quantitative

parameters derived from in vitro human cell and tissue models are being used

in conjunction with physiological models to predict metabolism, transport,

clearance, and pharmacodynamic outcome with increasing sophistication

and success 115. A recent report from the U.S. National Research Council,

commissioned by the EPA, states that: “Advances in toxicogenomics, bioin-

formatics, systems biology, epigenetics, and computational toxicology could

transform toxicity testing fromwhole animal testing to one founded primarily

on in vitromethods that evaluate changes in biologic processes using cells, cell

lines, or cellular components, preferable of human origin”.116

The in vitro reconstructed human skin, such as EpiskinÒ (SkinEthic) or

EpiDermTM (MatTek), is a biological model widely used in safety or efficacy


prescreening tests and is of growing interest for regulatory purposes as an

alternative to animal testing.117 This skin model consists of normal human

epidermal keratinocytes that form a stratified, highly differentiated,

organotypic tissue model of the human epidermis.118 Thus, this model

can be used as a metabolically active tissue and a physiologic barrier.

Metabolism is an important event to consider in genotoxicity and skin sen-

sitization evaluation. Hence, reconstructed human skin can provide the

means to investigate metabolism and toxicity of cutaneous exposures to

chemicals.118,119 Compared with cell models, a broad variety of chemicals

with different chemical properties can be evaluated in this skin model

(e.g., compounds with different pH and physical state) and is much

closer to physiological conditions than a monolayer cell culture.

Alternative end points, such as mutation and gene expression changes,

can also be investigated in this skin model.

The in vitro three-dimensional model of respiratory tract tissue, Epi-

AirwayTM (MatTek), consists of normal, human-derived tracheal/bronchial

epithelial cells that form a pseudostratified, highly differentiated model

which closely resembles the epithelial tissue of the respiratory tract. This air-

way model may provide the means to investigate barrier properties of the

airways, chemical-metabolizing capabilities, and gas-phase exposure of

volatile materials in airway inflammation and irritancy studies as well as

inhalation genotoxicity studies to occupational and environmental

chemicals.120 For example, measurable toxic responses in EpiAirway tissue

model (e.g., decreased cell viability, epithelial resistance, and elevated cyto-

kine release) were induced following treatment with tobacco smoke com-

ponents (i.e., nicotine, formaldehyde, cadmium, and urethane).121

Thus, improved cell culture technologies and the recognition that stud-

ies using cultured human cells and tissues can play an important role in stud-

ies of toxicity, metabolism, and transport have resulted in an increasing

utilization of these models in toxicological risk assessment and in quantita-

tive modeling of metabolism and transport processes.115 These in vitro studies

with human tissues can be integrated into PBPK models of in vivo exposure

scenarios. The in vitro kinetic data may be used to estimate metabolic

parameters for PBPK models, which can then estimate internal tissue con-

centrations in humans when ranges of chemical exposures are known. Thus,

use of these approaches with human in vitro systems in human health risk

assessments can not only reduce the use of animals in toxicology studies

but also help to decrease the uncertainties of extrapolating animal toxicity

data to humans.


6. RESEARCH NEEDS AND DATA GAPS

6.1. Developing and conducting exposure monitoringstudies

Dermal exposure is much less understood compared to inhalation exposure

despite the well-accepted recognition of the importance of the dermal route

on risk of developing disease. Historically, efforts to control occupational

exposures to hazardous agents have focused on inhalation rather than dermal

exposure. As a result, the current methods applied in dermal sampling are

nonstandardized and suffer from methodological issues. Also, the various

techniques employed in dermal exposure assessment have not been ade-

quately compared. Therefore, standardized dermal sampling techniques

need to be developed that accurately characterize exposure to the skin,

are noninvasive, and are easy to implement. PBPK models could be con-

ducted to not only investigate ADME of various chemicals but also compare

dermal sampling techniques (e.g., tape stripping vs. wipe sampling) in sample

recovery and association with relevant biomarkers. Dermal measurements

and their association with biomarkers need to be established to demonstrate

suitability in risk assessment. Additionally, little work has been done to assess

the effectiveness of workplace strategies and equipment designed to protect

against dermal exposure. Such studies would investigate and aid in filling-in

key data gaps in the field of dermal risk assessment and management.

While assessment strategies and methods are standardized and well devel-

oped for inhalation exposure in the workplace, there continues to be research

needs to further develop inhalation risk assessment and management. The

introduction and subsequent changes in health and safety legislation (i.e.,

Occupational Safety andHealth Act), changes and reduction in occupational

exposure limits, and the implementation of guidance on control and good

practice are several factors that may have had some impact on temporal

trends in inhalation exposure.122 The efforts to track temporal trends in ex-

posure levels have provided information for legislators to build upon existing

programs and develop new strategies aimed at reducing occupational expo-

sures and achieving long-term occupational health targets. However, the

collection and storage of new measurements appear to be decreasing, partic-

ularly if data collection is compliance-driven.122 Continued collection and

storage of quantitative exposure data is important to monitor changes in

exposure levels, which would help evaluate the effectiveness and inform

the future direction of initiatives aimed at reducing inhalation exposures.


The introduction of new, potentially hazardous chemicals in the workplace

requires continued validation of inhalation exposure assessment strategies

and collection of longitudinal data sets containing inhalation exposure mea-

sures and other workplace variables. Accurate exposure measurements are

also necessary to detect gene–environment interactions, which will be im-

portant components of future risk assessment strategies.

6.2. Interpreting biological monitoring studiesBiological monitoring data can provide important information on exposure

and uptake of a variety of occupational and environmental chemicals. The

data collected can be integrated with exposure characterization, PBPK

modeling, and animal toxicity data to inform risk assessment. However, crit-

ical knowledge gaps exist, which add to the uncertainty of the interpretation

of biological monitoring data. Filling these knowledge gaps is essential to

reduce these uncertainties, thus providing the most reliable data for risk

management and public health decisions.5

In September 2004, the Health and Environmental Research Institute,

EPA, Centers for Disease Control and Prevention, Agency for Toxic

Substances and Disease Registry, and the International Council of Chemical

Associations cosponsored the International BiomonitoringWorkshop to ex-

plore processes and information needed for placing biological monitoring

data into perspective for risk assessment. Based on the workshop, activities

in several areas to advance scientific understanding and application of bio-

logical monitoring data were proposed.5 One important proposed research

focus was to strengthen the understanding of the predictive relationships/

linkages between measures of exposure, dose, and effect. This insight would

allow the development of an interpretation strategy and specific criteria for

filling data gaps in the source–disease framework. Continued emphasis on

biomarker validation and analytical precision of biomarker measurements

(e.g., interlaboratory comparison) was also proposed for establishing their

application in exposure assessment. In addition, characterizing a baseline

for biomarker levels and applying statistical methods to assess temporal

departures from baseline can aid in identifying subpopulations at increased

risk of exposure or understanding factors affecting exposure. Biological

monitoring study designs could be improved to better assess intra- and inter-

individual variability related to measures of exposure, dose, metabolism, and

health effects, which would influence the chance of observing predictive

relationships between these variables. These studies would also clarify the


relevance of biomarkers for the target tissues of interest. Lastly, new tech-

nologies in the area of genomics and systems biology could be applied to

develop potential biomarkers and used as screening tools for identifying can-

didates for biological monitoring.5

6.3. Evaluating toxicogenomic studies for risk assessmentApproaches for evaluating genomic data for risk assessment should be both sys-

tematic and flexible enough to accommodate different health and risk assess-

ment practices.This includes steps to evaluate the available genomic data set for

its application to a broad range of information types (e.g., toxicodynamics,

toxicokinetics, intra- and interspecies differences in toxicokinetics and

toxicodynamics) that are useful to risk assessment. Toxicity, human, and tox-

icogenomic data sets can be considered together to determine the relationship

between gene and pathway changes to health or toxicity outcomes.

In order to facilitate the use of genomic data into human health risk as-

sessment, EPA has identified several approaches for incorporating informa-

tion on genetic polymorphisms into risk assessment strategies 112. The

conversion of genotype-based information into enzyme variability distribu-

tions may provide useful input into modeling internal dose (i.e., PBPK

modeling) usingMonte Carlo analysis approaches that better capture human

variability in internal dose and risk. One approach would be to prioritize

pathways that may most contribute to pharmacokinetic variability for a

given chemical based on analysis of genotype influence on enzyme activity.

Because PBPK modeling is resource-intensive, the analysis of variability in

enzyme activity is an important first step when deciding whether to describe

variability in internal dose via PBPK modeling. In addition, a screening

approach could be developed utilizing a pathway-specific pharmacokinetic

adjustment factor, which would be supported by in vivo pharmacokinetic

data or with biomarker data (e.g., DNA adducts). Lastly, polymorphism-

based changes in enzyme function may be incorporated into a PBPK model

so that the influence of variability in a single pathway can be assessed in a

framework that takes into account other pharmacokinetic factors (e.g.,

blood flow, organ size). Therefore, enzyme activity would be represented

by a population distribution rather than a single point-estimate rate constant.

The output from a PBPK/Monte Carlo analysis would depict the full dis-

tribution of internal doses and possible risks across the population. As the

number and types of genomic studies performed increases, genomic data will

likely inform multiple steps of the risk assessment process beyond the mode


of action. These approaches to convert genomic data into enzyme variability

distributions, through prioritizing pathway analysis, developing pathway-

specific pharmacokinetic adjustment factors, and incorporating genetic

polymorphism data into PBPK modeling, may be performed to facilitate

the use of genomics in risk assessment.112

As the use of genomewide association studies to identify genetic and epi-

genetic polymorphisms associated with intermediate phenotypes and disease

becomes more frequent, there is increasing interest in evaluating individual

genome interaction with environmental factors. Expert assessment of expo-

sure has been reported to provide greater statistical power than other

methods (e.g., job exposure matrices) for detecting associations between

exposure and disease.123 In the analysis of gene–environment effect modi-

fication, statistical power becomes an even greater issue as studies typically

require large sample sizes to detect effect modification.124 Even small errors

in the assessment of environmental factors can result in increased sample size

requirements for the detection of effect modification.125 Thus, approaches

for obtaining the highest quality exposure data in gene–environment inter-

action studies need to be established and tested.

7. CONCLUDING REMARKS

Aworker’s risk of developing an occupationally related disease, such as

allergic contact dermatitis or diisocyanate-induced asthma, may result from a

unique combination of exposure; genetic/epigenetic variation; and various

individual, workplace, and lifestyle factors. This chapter highlights and dis-

cusses important areas and data gaps within the exposure–disease framework

and how toxicology can be utilized in the context of occupational exposure

assessment to unlock the black-box paradigm. Work practices, such as pro-

tective clothing or equipment used and duties performed at work, may

influence the amount of exposure received. The exposure route and biolog-

ical membrane(s) involved in uptake will drive the ADME of chemicals and

location of the subsequent toxic action in the body. A person’s genome/

epigenome may dictate the metabolism of chemicals, repair mechanisms,

and rate of uptake and clearance of chemicals from the body. Recent ad-

vances in toxicogenomics, systems biology, computational methods, in vitro

systems, and analytical methods for quantifying exposures and biomarkers

have generated interest in investigating genome–environment modifications

on risk of developing disease. In addition, recent scientific evidence suggests

that the success of finding such modifications relies on the quality of


exposure assessment measures. Future work should consider standardizing

and collecting high-quality longitudinal exposure measures, validating bio-

markers and investigating their relationship with exposure and health effects,

and facilitating the use of genomic data into risk assessment. Development of

a multidisciplinary approach including longitudinal exposure assessment

with biomonitoring, epidemiology, toxicology, and genomics will be

important in shaping the future direction of risk assessment and risk manage-

ment strategies to protect worker health.

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