Human variability in hepatic and renal elimination: implications for risk assessment

HUMAN VARIABILITY IN HEPATIC METABOLISM AND RENAL EXCRETION 411

Copyright © 2007 John Wiley & Sons, Ltd. J. Appl. Toxicol. 2007; 27: 411–420

DOI: 10.1002/jat

JOURNAL OF APPLIED TOXICOLOGYJ. Appl. Toxicol. 2007; 27: 411–420Published online 14 May 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/jat.1255

ReviewHuman variability in hepatic and renal elimination:implications for risk assessment

J. L. C. M. Dorne*

Division of Developmental Origins of Health and Disease, Institute of Human Nutrition, Clinical Pharmacology Group, School ofMedicine, University of Southampton, Biomedical Sciences Building, Bassett Crescent East, Southampton, SO16 7PX, UK

Received 9 March 2007; Accepted 12 March 2007

ABSTRACT: Hepatic metabolism and renal excretion constitute the main routes of xenobiotic elimination in humans.

Improving human risk assessment for threshold contaminants requires the incorporation of quantitative data related

to their elimination (toxicokinetics) and potential toxic effects (toxicodynamics). This type of data provides a scientific basis

to replace the standard uncertainty factor (UF ===== 10) allowing for the consideration of human variability in toxicokinetics

and toxicodynamics. This review focuses on recent research efforts aiming to incorporate human variability in hepatic

and renal elimination (toxicokinetics) into the risk assessment process. A therapeutic drug database was developed to

quantify pathway-related variability in human phase I and phase II hepatic metabolism as well as renal excretion in

subgroups of the population (healthy adults, neonates and the elderly), using data on compounds cleared primarily

through each route (>>>>> 60% dose). For each subgroup of the population and elimination route, pathway-related UFs were

then derived to cover 95–99% of each subgroup. Overall, the default toxicokinetic UFs would not cover neonates,

the elderly for most elimination routes and any subgroup of the population for compounds metabolized via polymorphic

isozymes (such as CYP2C19 and CYP2D6). These pathway-related UFs allow the incorporation of in vivo metabolism

and toxicokinetic data in the risk assessment process and provide a flexible intermediate option between the default

UF and chemical-specific adjustment factors (CSAFs) derived from physiologically based pharmacokinetic models.

Implications of human variability in hepatic metabolism and renal excretion for chemical risk assessment are discussed.

Copyright © 2007 John Wiley & Sons, Ltd.

KEY WORDS: human variability; pharmacokinetics; toxicokinetics; hepatic metabolism; renal excretion; pathway-related

uncertainty factors; risk assessment

Introduction

In the modern world, humans are exposed to a wide

range of natural and synthetic chemicals, and a number

of questions have been raised as to whether these sub-

stances could cause adverse health effects through the

adulteration of food, beverages and the environment1.

As early as the first century AD, Pliny the elder wrote

‘So many poisons are employed to force wine to suit our

taste and we are surprised that it is not wholesome!’.

During the 16th century, the physician and alchemist

Paracelsus observed silicosis in miners as an example of

chronic occupational exposure and was one of the first to

consider the relationship between the dose of a chemical

and a toxic response with its famous aphorism ‘Sola

dosis fecit venenum – it is only the dose which makes a

chemical a poison’. Later, Sir Percival Pott conducted

one of the first epidemiological studies and correlated

occupational exposure with scrotal cancer in young

British chimney sweeps. During the 20th century, the

complex process of chemically induced toxicity has been

classified to consist of five stages (Beck et al., 1994):

(1) exposure and penetration of the chemical into the

target organism;

(2) delivery of the toxicant to its site of action;

(3) interaction of the toxicant with the target (cell and

molecular components);

(4) early toxic response after the initiating interaction

which can be described in histopathological, physio-

logical and biochemical terms; and

(5) clinical symptoms of the intoxication.

* Correspondence to: J. L. C. M. Dorne, Unit on Contaminants in the Food

Chain, European Food Safety Authority, Largo N. Palli 5/A, 43100 Parma,

Italy. Tel: +39-0521036472, Fax: +39-0521036572.

E-mail: [email protected]

Contract/grant sponsor: Department of Health, Health and Safety Executive

(1998–2001 UK).

Contract/grant sponsor: Health Canada (2002–2004).

Contract/grant sponsor: European Commission (2004–2006 under the NO

MIRACLE); contract/grant number: 003956.1 Over 100 000 natural substances have been identified (with many more for

which the structures have not been determined) and more than five million

man-made chemicals of which 70 000 are in commercial use.

412 J. L. C. M. DORNE


DOI: 10.1002/jat

Traditionally in human risk assessment, safe levels of

exposure for food and environmental contaminants are

derived using two approaches: quantitative risk assessment/

cancer assessment and non-quantitative risk assessment/

non cancer assessment. Quantitative risk assessment/cancer

assessment is performed for genotoxic and carcinogenic

substances whereas non quantitative risk assessment/

non-cancer assessment for compounds assumed to show

a threshold (non-genotoxic compounds) below which no

toxicity occurs. There is a general consensus within the

scientific community to harmonize these approaches since

they are both divided by four sequential steps: hazard

identification, hazard characterization, exposure assess-

ment and risk characterization but have been separated

historically (Agenda 21 of the United Nations Conference

on the Environment and Development (UNCED) 1992

Earth Summit; European Centre for Ecotoxicology

and Toxicology of Chemicals (ECETOC) workshop

(10 November 2000, Brussels). Each of these steps has

been reviewed elsewhere (Barlow et al., 2002; Edler

et al., 2002; Dybing et al., 2002; Kroes et al., 2002;

Smith, 2002; Renwick et al., 2003).

In quantitative or cancer risk assessments, the human

health risk is associated with an estimated exposure or

vice versa and these are quantified usually using dose-

response relationships often based on experimental animal

data combined with low dose extrapolation (Dorne, 2004).

In contrast, for a non-quantitative/non cancer risk assess-

ment, the threshold approach aims to set levels of

exposure ‘without appreciable health risk’ and these are

expressed in mg kg−1 of diet per day to relate it to human

oral exposure. The nomenclature for these safe levels varies

between countries and regulatory agencies, i.e. acceptable

daily intake (ADI) (WHO and International Programme

on Chemical Safety), the estimated-concentration-of-no-

concern (ECNC) in the Netherlands, the tolerable daily

intake or tolerable concentration in Canada or the refer-

ence dose/concentration (RfD/RfC) in the US Environ-

mental Protection Agency (EPA) (Truhaut, 1991; Dourson

et al., 1996; WHO, 2001; USEPA, 2002). Despite the

nomenclature differences, these safe levels are usually

determined by dividing a surrogate for the threshold

determined from chronic/subchronic studies using the

most sensitive animal species (usually mouse, rat, rabbit

or dog), such as the no observed adverse effect level

(NOAEL) or the benchmark dose (BMD), by an uncer-

tainty factor of a 100-fold (Crump, 1984; WHO, 1987;

Dourson, 1996; Dorne and Renwick, 2005). The ration-

ale for the use of the 100-fold uncertainty factor has

remained unclear historically since its original introduc-

tion by Lehman and Fitzhugh (1954) and many research

efforts have been undertaken to refine its use and scien-

tific basis (Dourson et al., 1996, Hattis et al., 1987;

Renwick, 1991, 1993; Hattis, 1996; Naumann et al.,

1997; Renwick and Lazarus, 1998; Renwick et al.,

2000; Silvermann et al., 1999; Naumann et al., 2001;

Vermeire et al., 1999; Dorne et al., 2005; Ginsberg et al.,

2005).

A critical aspect to refine uncertainty factors is to

incorporate differences between individuals of the human

population (genetic polymorphism, differences between

healthy adults, neonates, children and the elderly) at

the level of xenobiotic metabolism and elimination (toxico-

kinetics) as well as at the level of toxicity in the target

organism/organ/cell/receptor (toxicodynamics) (Renwick,

1991, 1993; Dorne et al., 2001a; Dorne, 2004).

This review focuses on the importance of human vari-

ability in toxicokinetics with particular reference to the

major elimination routes in man (i.e. hepatic metabolism

and renal excretion) and how quantitative knowledge of

this varaibility may provide options to improve chemical

risk assessment. First, the history of uncertainty factors

with reference to our increasing knowledge better to

define their scientific basis will be presented. Then, the

results of studies quantifying human variability in hepatic

metabolism and renal excretion will be examined together

with their implications for risk assessment.

History of Uncertainty Factors

The 100-fold uncertainty factor was introduced in the

United States in the mid 1950s to define legislative

guidelines for food additives and environmental contami-

nants. It was originally proposed that the human safe

level ‘without appreciable health risk’ could be derived

from a NOAEL by dividing it with a safety/uncertainty

factor of a 100-fold. In 1961, the scheme was adopted in

Europe by the Joint FAO/WHO Expert Committee on

Food Additives (JECFA) and by the Joint FAO/WHO

Expert on Pesticides Residues (JMPR). The safe level

was defined as the ADI under the instigation of Pr Rene

Truhaut as ‘the daily intake of chemical which, during

the entire life time, appears to be without appreciable risk

on the basis of all known facts at the time’ (Truhaut,

1991).

The original investigators, Lehman and Fitzhugh

(1954), defined the 100-fold default factor and reasoned

that it allowed for several areas of uncertainty:

— interspecies variability (extrapolation from the experi-

mental animal to man),

— human variability (extrapolation from an average

healthy adult to sensitive individuals of the popula-

tion), and

— possible synergistic effects (to prevent eventual toxic

outcome of contaminants).

The rationale behind the refinements of the default factor

approach was brought about with the evolution of the

science of biochemistry and toxicology which led scien-

tists and regulators to realise that a single default factor



DOI: 10.1002/jat

would not cover the differences and the complexity of the

wide range of metabolic reactions and mechanisms of

toxicity in test laboratory species or/and in humans.

Calabrese (1985) investigated interindividual differ-

ences in the metabolism of xenobiotics and found that

the 10-fold safety factor would protect 80–95% of the

human population but the author assumed that the 10-fold

could account for the total range of human variability

(Calabrese, 1985; Dourson, 1996). Hattis et al. (1987)

examined a set of 101 toxicokinetic parameters for 49

compounds and concluded that 96% of the observed

human variability was covered by the default factor of 10

(Hattis et al., 1987). A criticism was that certain sub-

groups of the population were not included in the ana-

lysis (influence of genetic polymorphism, age, etc and the

authors speculated that a greater number of potentially

sensitive individuals would probably be at risk compared

with the ‘average healthy adult’ (Hattis et al., 1987;

Dourson, 1996).

The basic biochemical processes that are involved

in the generation of adverse effects following chemical

exposure have been recognised to be dependent on the

movement and disposition of the toxicant in the body

(toxicokinetics, TK) and the expression of its toxicity

after reaching the target organ(s) (toxicodynamics, TD)

(Renwick, 1991, 1993). The TK aspect is dependent on

the processes relating the external dose and the internal

dose: absorption of the chemical from the site of admini-

stration, its distribution, metabolism and excretion. The

TD aspect is dependent upon the concentration of the

proximate toxicant (parent compound, metabolite or both)

in the target organ(s) and the sensitivity of the target

organ(s) itself (Renwick, 1991, 1993).

For evident ethical reasons, quantitative data describ-

ing the TK and TD of food additives and contaminants

are scant in humans and the rationale for the human vari-

ability factor has been mainly investigated using TK

parameters reflecting chronic and acute exposures from

the extensive database on therapeutic drugs (Hattis et al.,

1987; Renwick, 1991, 1993; Hattis, 1996; Naumann

et al., 1997; Renwick and Lazarus, 1998; Silvermann

et al., 1999; Ginsberg et al., 2005). The parameters of

choice were the clearance and area under the plasma con-

centration versus time curve (AUC) for chronic exposure

since these reflect the chronic blood concentration and

body burden and, the maximum concentration in plasma

(Cmax) for acute exposure. For these analyses, well recog-

nised assumptions were formulated and they relate to the

fact that kinetic and dynamic data follow log-normal dis-

tribution and that the interindividual variability, observed

following single doses would reflect chronic exposures

(Renwick and Lazarus, 1998; Silverman et al., 1999).

Renwick (1991) analysed the validity of the 100-

fold factor by subdividing the interspecies (×10) and the

human variability factor (×10) into four equal factors of

100.5 (3.16) to allow for the TK (×3.16) and TD (×3.16)

differences. Subsequently, the author analysed a small

database describing interspecies differences, expressed as

the ratio between the animal species and humans for TK

processes and parameters (e.g. liver weight, liver blood

flow, renal blood flow, absorption, elimination) as well as

for TD sensitivity to a chemical (e.g. sedation, pain

relief). The author came to the conclusion that the 10-

fold default factors could be subdivided to allow for TK

(100.6 = 4.0) and TD (100.4 = 2.5) (Renwick, 1993). The

aim of this subdivision was to allow chemical-specific

TK and mechanistic data to contribute quantitatively to

the selection of the uncertainty factor (Renwick, 1993).

For example, when animal or human data on a particular

chemical are available in either areas of uncertainty, the

default factors can be replaced by a chemical-specific

adjustment factor (CSAF) usually derived from a physio-

logically based pharmacokinetic (PB-PK) model (WHO,

2001, 2005). The principle of subdivision was accepted

by the International Programme on Chemical Safety

(IPCS) workshop on the derivation of guidance values,

and modified to allocate an even factor (100.5 = 3.16) for

both TK and TD differences among humans, whereas the

interspecies uncertainty factors remained as the author

suggested (WHO, 1994).

A more recent analysis evaluated human variability

using a database of 60 therapeutic drugs representing a

range of metabolic and elimination pathways (Renwick

and Lazarus, 1998). The analysis of the kinetic data

revealed a coefficient of variation of 38% for the kinetic

aspect (range: 9–114%) and 51% for dynamics (range: 8–

137%). Importantly, the authors argued that the database

on TD included patients undergoing treatment and the

disease processes could have contributed to higher vari-

ability in the reviewed responses. Overall, the analysis

supported the even subdivision for kinetics and dynamics

(100.5 or 3.16); however, the default kinetic factor could

not cover human variability for polymorphic metabolism

(such as CYP2D6) or differences between healthy adults

and neonates. This study led to the proposal that a

number of categorical-default uncertainty factors could be

generated for both the interspecies differences and the

human variability. Physiological differences can be used

to quantify TK differences between species (species

specific-categorical default factors) or differences in

metabolic pathways, for both interspecies and human

variability (pathway-related uncertainty factors, described

in the next section). For the TD aspect, quantitative

knowledge for different classes of toxicity mechanisms

can be used to derive class-effect specific uncertainty

factors (Renwick and Lazarus, 1998; Dorne and Renwick,

2005). Such uncertainty factors could constitute an inter-

mediate option between current default factors and the

ideal CSAFs (WHO, 1999, 2001, 2005). It also allows

for flexibility since CSAFs for kinetics and/or dynamics

(for either interspecies, human variability or both) can be

applied in the presence of a PBPK model or current



DOI: 10.1002/jat

default values can be used in the absence of chemical-

specific data.

Human variability in hepatic metabolism and renal

excretion and pathway-related uncertainty factors.

The approach proposed by Renwick and Lazarus

(1998) has been explored using the therapeutic drug data-

base for major routes of hepatic metabolism and renal

excretion in subgroups of the population (healthy adults

from different ethnic origins and different age groups

(neonates, children and the elderly) (Dorne et al., 2001a,

2001b, 2002; 2003a, 2003b, 2004a, 2004b, 2005). Path-

ways for hepatic metabolism were classified into phase I

(CYP isoforms (CYP1A2, CYP2E1, CYP2C9, CYP2C19,

CYP2D6, CYP3A4), alcohol dehydrogenase (ADH),

esterases) and phase II (N-acetylation, glucuronidation

(UDP-glucuronyltransferases), glycine and sulphate

conjugation), and further subdivided into functionally

monomorphic and polymorphic enzymes. Since not all

polymorphisms have been characterized with regard

to pharmacokinetics, when no in vivo kinetic data were

available in phenotyped individuals (i.e. extensive

and poor metabolizers), the pathway was classified as

monomorphic. Therefore, CYP1A2, CYP2E1, CYP3A4,

ADH and esterases (hydrolysis) as well as CYP2C9,

CYP2C19 and CYP2D6, were regarded as monomorphic

and polymorphic phase I pathways, respectively. For

phase II pathways only N-acetyltransferase 2 was classi-

fied as polymorphic (Dorne, 2004; Dorne et al., 2005;

Dorne and Renwick, 2005). This categorization might

change in the future with emerging data on enzyme

polymorphism (Miners et al., 2002; Chen et al.,

2005; Krishna and Shekar, 2005; Mouly et al., 2005;

Costa, 2006).

Major probe substrates for each elimination route

(> 60% of an oral dose eliminated by the pathway) were

selected providing their oral absorption was complete

so that variability arose from metabolism not absorption

(see Dorne et al., 2001a). For each probe substrate and

subgroup of the human population, pharmacokinetic

variability was analysed from published studies using

parameters reflecting chronic exposure (metabolic and

total clearances, area under the plasma concentration

versus time curve (AUC)) and acute exposure (Cmax)

(data not shown). Subpopulations in this analysis included:

generally healthy adults (16–70 years of age, mostly of

Caucasian origin or of unreported ethnicity), the elderly

(>70 years), children (>1 year to <16 years), infants (>1

month to <1 year) and neonates (<1 month). Individual

pharmacokinetic studies were transformed onto the log

scale (characterized by geometric means (GMs) and

standard deviations (GSDs) and coefficient of variation

(CVs)) and meta-analyses were performed across com-

pounds for each subgroup and pathway to quantitate

pathway-specific variability (Dorne et al., 2002). Com-

parison between healthy adults and each subgroup to

determine differences in internal dose (elimination rate)

for chronic and acute exposure was achieved using ratios

of geometric means, i.e. a value >1 indicates an increase

in internal dose (decrease in elimination) in the subgroup.

Individual ratios were then averaged on the log-scale

to define subgroup-specific pathway-related differences.

Pathway-related uncertainty factors were derived for

each metabolic route and subgroups to cover the 95th,

97.5th and 99th centiles (quoted here) using CVs for

healthy adults and for subgroups (Dorne et al., 2001a,

2001b, 2002). Tables 1 and 2 summarize pathway-

specific differences in internal dose (ratio of geometric

means) for subgroups of the population and the corre-

sponding pathway-related uncertainty factors (99th

centile) for circumstances in which the current kinetic

default uncertainty factor (3.16) would not provide a

sufficient degree of protection for healthy adults, children,

neonates and elderly.

Monomorphic Pathways in Healthy Adults

Human variability for monomorphic phase I (CYP1A2,

CYP2A6, CYP2E1, ADH and hydrolysis) and phase II

(glucuronidation, glycine and sulphate conjugation) meta-

bolism as well as renal excretion was low in healthy

adults with CVs ranging from 21% to 31% for the oral

route. For all these elimination routes, pathway-related

UFs (1.6–2.2, 99th centiles) were below the default

kinetic uncertainty factor (3.16).

CYP3A4 metabolism was the most variable of the

monomorphic pathways for the oral route with a CV of

46% (UF = 2.8), whereas variability for the intravenous

route was 32% (Dorne et al., 2003a). These differences

are mostly due to the intestinal vs hepatic expression of

CYP3A4, polymorphism of several allelic and protein

variants related to CYP3A4 (but with limited clinical

relevance yet) as well as competition between CYP3A4

substrates and P-glycoprotein in the gastrointestinal tract

(together with P-glycoprotein polymorphism) (Wacher

et al., 1995; Suzuki and Sugiyama, 2000; Cummins

et al., 2002; Dorne et al., 2003a, 2005).

Polymorphic Pathways in Healthy Adults

Inter-individual differences for polymorphic phase I

(CYP2C9, CYP2C19, CYP2D6) and phase II (N-

acetyltransferase-2, NAT-2) metabolism were available

for non-phenotyped subjects (NPs), extensive and poor

metabolizers (EMs and PMs, respectively for CYP2C9,

CYP2C19 and CYP2D6) as well as fast acetylators

(FAs) and slow acetylators (SAs) (NAT-2). Variability

for CYP2C9 metabolism in NPs, homozygous and hetero-

zygous EMs and PMs was low, and ranged from 12%

to 32% in all subgroups (UF = 1.5–2.1). PMs showed

lower clearances in heterozygous PMs compared with



DOI: 10.1002/jat

Table 1. Pathway-related toxicokinetic uncertainty factors exceeding the default uncertainty factor in healthyadults (modified from Dorne et al., 2005)

Pathway-related

Ratiouncertainty factors

Pathway Nc Ns N CVLN GM 95th 99th

Healthy adults

CYP2C19 (EM) 3 7 56 60 2.5 3.8

CYP2C19 (PM) 3 4 21 20 31 45 52

CYP2D6 (NP) 8 41 520 63 3.0 4.7

CYP2D6 (EM) 9 24 192 66 3.5 5.8

CYP2D6 (PM) 7 13 74 29 9.0 21 26

NAT (SA) 2 16 472 22 3.1 4.4 5.2

African healthy adults

CYP2D6 (NP) 1 1 10 64 1.1 2.9 4.3

CYP2D6 (EM) 1 1 18 120 1.8 8.2 15

CYP3A4 2 21 42 1.5 2.9 3.3 3.8

Asian healthy adults

Hydrolysis 1 1 12 43 2.0 3.8 5.0

CYP2C19 (EM) 2 6 40 63 1.7 4.4 6.6

CYP2C19 (PM) 2 6 34 27 15 24 28

NAT (FA) 1 1 33 34 1.8 3.1 3.9

NAT (SA) 1 1 5 39 2.8 5.2 6.7

Nc, number of compounds in the database; Ns, number of studies in the database; n, number of subjects in the database; CVLN, mean coefficient of varia-

tion; Ratio GM, ratio of the clearances (geometric means) between non-phenotyped and phenotyped general healthy adults (mostly of Caucasian origin) and

African/Asian healthy adults (NP, EM, PM). NP, non-phenotyped healthy adults; EM, extensive metabolizers; PM, poor metabolizers; FA, fast acetylators;

SA, slow acetylators.

homozygous EMs (Dorne et al., 2004b). Based on limited

data (n < 15), the default factor (3.16) would not appear

to adequately protect individuals carrying the *3/*3

alleles and a CYP2C9-related uncertainty factor of 6.5

would be necessary to cover 99% of this subgroup.

In vivo data for CYP2C9 PMs are emerging in the litera-

ture facilitating an updated assessment of the CYP2C9

database (Kirchheiner and Brockmoller, 2005; Dorne,

unpublished).

Human variability for CYP2C19 and CYP2D6 was large

with 44/60% and 63/66% in NPs and EMs respectively

and variability in PMs was lower than that in EMs (20/29%)

but was associated with a 31-fold and 9-fold increase in

internal dose (Dorne et al., 2002, 2003b). Inter-individual

Table 2. Pathway-related toxicokinetic uncertainty factors exceeding the default uncertainty factor (3.16) inchildren, neonates and the elderly (modified from Dorne et al., 2005)

Pathway-related

Ratiouncertainty factors

Pathway Nc Ns n CVLN GM 95th 99th

Children

CYP2C19 1 1 25 86 1.6 5.4 9.0

CYP2D6 1 2 173 140 4.0 22 45

Neonates

CYP1A2 2 7 251 35 6.2 11 14

CYP3A4 2 5 35 65 3.0 8.1 12

Glucuronidation 4 14 94 50 3.9 8.6 12

Glycine Conjugation 2 1 10 16 19 25 28

Renal excretion 7 33 656 32 1.7 2.8 3.4

The elderly

CYP2C19 1 1 10 39 1.8 3.4 4.3

CYP2D6 (NP) 5 7 69 88 1.4 5.0 8.4

CYP3A4 10 15 163 46 1.8 3.6 4.9

NAT (SA) 2 4 105 29 3.9 6.3 7.6

Renal 6 8 105 33 2.0 3.3 4.2

Nc, number of compounds in the database; Ns, number of studies in the database; n, number of subjects in the database; CVLN, mean coefficient of varia-

tion; Ratio GM. ratio of the clearances (geometric means) between non-phenotyped general healthy adults and children, neonates and the elderly; NP, non-

phenotyped healthy adults; SA, slow acetylators.



DOI: 10.1002/jat

differences in NAT-2 metabolism for FAs and SAs were

relatively low with CVs of 32% and 22%. For CYP2D6

metabolism, high human variability in TK is associated

with a range of number of copies of the gene in differ-

ent individuals: 13 copies in super-fast oxidizers, upto

four copies in intermediate individuals and no copies in

‘strict’ PMs (Dalen et al., 1998, 1999). The CYP2C19 and

NAT-2 genes have also been demonstrated to be poly-

allelic with 8 and 14 variant alleles and would predict the

PM and SA status (Lin et al., 1993; Wedlund, 2000).

The analysis of human variability for polymorphic

routes of xenobiotic metabolism assumed that the proxi-

mate toxicant was the parent compound since PMs or

SAs would be the susceptible subgroup so that a decrease

in clearance would increase the risk. However, the reverse

situation is also commonly seen, i.e. metabolic activation

to a toxic species so that the EM or the FA subgroup

would be the susceptible subgroup (Dorne et al., 2002,

2003b, 2005). Toxic metabolites are produced after bio-

activation of compounds such as the organophosphoro-

thioate chlorpyrifos by CYP2D6, CYP2C19, CYP1A2

and CYP3A4 (Sams et al., 2000, 2004; Buratti et al.,

2003; Costa et al., 2003) CYP2C19, CYP2C9 and NAT-

2 are known to activate methoxychlor (Tang et al., 2001;

Stresser and Kupfer, 1998; Rose et al., 2005) and

heterocyclic amines (Land et al., 1989).

The TK data and analyses reviewed in this article were

based on compounds showing a range of clearance

values. Even though systematic analysis of the relative

contribution and importance of enzyme capacity limita-

tion or perfusion limitation of hepatic metabolism was

not undertaken, the oral toxicokinetic data analysed by

Dorne and Renwick (2005) reflect the overall effects of

bioavailability and systemic clearance. In the case of

poorly extracted compounds, inter-phenotypic differences

are reflected mostly by difference in enzyme activity

as it affect both systemic clearance and bioavailability.

On the other hand, for highly extracted compounds oral

TK data can be assumed to provide a reasonable indica-

tion of the impact of differences in hepatic metabolism.

The relative importance of blood flow vs enzyme capa-

city limitation of hepatic metabolism may change from

one dose level to another as well as among individuals

with polymorphic expressions. When data or models

facilitate the evaluation of the impact of these critical

determinants of hepatic metabolism, those should be

undertaken to gain a better insight of the impact of poly-

morphic pathways on the magnitude of UF (e.g. Gentry

et al., 2002; Nong et al., 2006).

Inter-ethnic Differences in Hepatic Metabolismand Renal Excretion

Human variability in TK derived for healthy adults from

different ethnic origins (African, Asian and South Asian

adults) was similar to average healthy adults with CV of

20–30% (UF = 1.2–3.0) for all monomorphic pathways

and renal excretion. No differences in internal dose

were found between healthy adults (mostly Caucasian)

and other ethnic groups (Dorne et al., 2005) apart from

CYP3A4 metabolism for which 1.5-, 2- and 3-fold in-

creases in internal dose were found between sub-Saharan

African, South Asian and Mexican adults, respectively.

Differences in CYP3A4 activity have been attributed

to lower hepatic clearance, gut metabolism and P-

glycoprotein activity in South Asian and African adults

compared with general healthy adults (Lindholm et al.,

1992; Rashid et al., 1995; Johnson, 2000).

Lower clearances were also observed for hepatic poly-

morphic metabolism in African populations compared

with healthy adults for CYP2D6 (2-fold in EMs, vari-

ability 120%) and in Asian populations for CYP2C19

and NAT-2 (but not CYP2D6) (15-fold in PMs and 2-

fold in EMs and 2-, 3-fold in FAs and SAs (Dorne et al.,

2002, 2003b)). For CYP2D6, there was virtually no

difference between Asian PMs and healthy adult NPs,

whereas Asian EMs had a higher clearance than healthy

adult EMs. These differences have been argued to be due

to differences in CYP2D6 frequency for PMs (2% in

Asian and 8% in Caucasians) such that the 99th centile of

PMs would represent only 0.02% of an Asian population

but 0.08% of a Caucasian population (Dorne et al., 2002,

2005). However, quite a different situation is observed

for the CYP2C19 pathway with 14–21% PMs in Asian

subgroups (up to 61% for the inhabitants of Vanuatu

Pacific island) compared with 3% in Caucasian subgroups

(Wedlund, 2000).

The Elderly

It is well documented that hepatic metabolism and renal

elimination are impaired in old age as a consequence of

ageing processes and significant reductions (1.5–3-fold)

were noticeable for most pathways for CYP3A4,

CYP2D6, CYP2C19 and SAs for NAT-2 (Durnas et al.,

1990; Le Couteur et al., 1999). No reliable data were

available for PM subjects in the elderly but data for eld-

erly NPs suggest lower CYP2D6 and CYP2C19 activity

compared with healthy adult PMs would be expected

(Dorne et al., 2002, 2003b; Dorne, 2004). A recent ana-

lysis has demonstrated 50–75% increase in the half-lives

of drugs metabolized by cytochrome P450 or eliminated

via renal excretion in individuals older than 65 years of

age (Ginsberg et al., 2005).

Children and Neonates

Toxicokinetics studies in children are available for CYP

metabolism, glucuronidation, glycine conjugation and



DOI: 10.1002/jat

renal excretion. For several elimination routes in neonates,

activity was shown to be lower compared with healthy

adults: 6-fold for CYP1A2, 3.0-fold for CYP3A4, 3.9-

fold for glucuronidation, 19-fold for glycine conjugation

and 1.7-fold renal excretion (Dorne et al., 2005). These

reductions have been related to the immaturity of a

number of enzymes in neonates (Aranda et al., 1980;

Besunder et al., 1988; Cazeneuve et al., 1994; Cresteil,

1998; Sonnier and Cresteil, 1998; Leeder et al., 2000;

Renwick et al., 2000; Ginsberg et al., 2002, 2004).

CYP1A2, CYP2C9, CYP2C19 and glucuronidase activity

are known to be lacking in neonatal livers and these

rise to attain half the adult activity during the first

6–12 months whereas CYP3A7, CYP2D6, CYP2E1 and

sulphation are expressed at birth to a much larger extent

(Treluyer et al., 1997; Koukouritaki et al., 2004; de Wildt

et al., 1999; Gow et al., 2001; Sonnier and Cresteil,

1998; Leeder et al., 2000). Neonatal conjugation of

glycine appears to be functional but highly saturable

(LeBel et al., 1988; Gow et al., 2001). Finally, low clear-

ances for compounds undergoing renal excretion in

neonates have also been revealed and this conclusion

relates to the ontogeny of glomerular filtration since it is

known to rise after birth and adult values are achieved

only at about 7 months (Besunder et al., 1988).

Human variability in polymorphic pathways in

neonates was only available for two PM subjects and

CYP2D6 metabolism (19- and 33- fold difference com-

pared with adults) (Ito et al., 1998). These limited data

suggest that exposure to chemicals metabolized via

CYP2D6 in neonates remains a concern since the default

UF may not protect them sufficiently (Dorne et al., 2002,

2005).

Implications for Chemical Risk Assessment

Human data quantifying variability in hepatic metabolism

and renal excretion in vivo have provided a basis to derive

pathway-related uncertainty factors, as an intermediate

option between the ideal chemical-specific adjustment

factors (CSAFs) and the default kinetic uncertainty factor

(3.16). These uncertainty factors give a greater degree of

flexibility to risk assessors and risk managers if the meta-

bolic fate of the particular compound under investigation

is known. Cell lines (expressing specific CYP isoforms),

liver microsomes and enzyme inhibitors may be used to

characterize metabolic routes in vitro (Venkatakrishnan

et al., 2001; Brandon et al., 2006). Tracer doses of the

compound can also be administered to a small group of

healthy adults to generate in vivo metabolism and excre-

tion data (Dorne and Renwick, 2005).

Tables 1 and 2 summarize situations for which the

kinetic UFs would not provide a sufficient degree of

protection for children (polymorphic CYPs), neonates

(all pathways: CYP1A2, CYP3A4, renal excretion,

glucuronidation, glycine conjugation) and the elderly

(CYP3A4, renal excretion). Examining the inter-individual

differences in hepatic metabolism via polymorphic

enzymes (CYP2C9, CYP2C19 and CYP2D6 and NAT-

2), the default kinetic UF would not be sufficient to cover

any human subgroup of the population for major probe

substrates of each polymorphic route (60–100% of an

oral dose in EMs or FAs). Further analyses of human

variability in kinetics for EMs and PMs and minor

substrates of the CYP2D6 and CYP2C19 isoforms (10–

60% metabolism) have established that an exponential

relationship relates the extent of metabolism in EMs

and difference in clearance rate between EMs and

PMs (and consequently the pathway-related uncertainty

factors) (Dorne et al., 2002; 2003b). These correla-

tions demonstrate that the kinetic UF would cover

differences between EMs and PMs for compounds

metabolized via CYP2D6 and CYP2C19 to a minor

extent (30% of an oral dose) (Dorne et al., 2002;

2003b). Such data are not available for NAT-2 and

CYP2C9 and the existence of such a relationship has

not been tested for these hepatic routes; however,

future work and emerging data may allow for such an

analysis. These findings highlight the need for human

metabolism data to assess the potential toxicity of

contaminants and in this case, it is critical to define the

toxicological consequence of metabolism since PMs/

SAs or EMs and FAs could be susceptible to toxicity,

if the parent compound or the metabolite were the

toxicant. An important question related to UFs is whether

to include either subgroup data separately or to use the

CSAF or pathway-related factor to protect a percentile

of the total population, e.g. frequencies of polymorphism

in Caucasian and Asian populations for CYP2D6 (8%

and 1%) and CYP2C19 PMs (2.5% versus 15%) and

NAT-2 (40–70% versus 10–20%) (Lin et al., 1993;

Wedlund, 2000). Given the ethnic diversity of the world

we live in, different uncertainty factors could be devel-

oped if the value were chosen to cover a particular

percentage of the total population. Other essential sub-

groups to consider are fetus and neonates even though

they represent a small fraction of the population at a

particular time point, they also characterize the whole

population in continuity. In relation to the latter issue,

a limitation of the current database is the paucity of

data (small number of compounds, studies and indi-

viduals) for a number of pathways and subgroups of the

population, e.g. extensive and poor metabolizers of

CYP2C9 and CYP2C19, among children and neonates.

However, pharmacokinetic studies are increasingly

emerging from the literature with particular reference to

polymorphic routes of metabolism, in children and

neonates, and future updates will be critical to incor-

porate these subgroups of the population in the risk

assessment process based on confident quantitative

evidence.



DOI: 10.1002/jat

Concluding Thoughts

The recent developments in pharmacology, toxicology

and molecular biology have been very valuable to toxi-

cologists and risk assessors in offering a number of

science-based approaches to develop uncertainty factors

and derive safe exposure levels of chemicals in humans.

This review has discussed inter-individual differences in

hepatic metabolism and renal excretion and how these

can be integrated into the risk assessment process. Situ-

ations for which standard uncertainty factors were too

conservative (monomorphic pathways of metabolism) or

did not cater for high variability in certain subgroups of

the population (genetic polymorphism, neonates, children

and the elderly) have been highlighted. The levels of

refinement of uncertainty factors rely heavily on scientific

evidence and therefore default uncertainty factors should

be used only when no data describing metabolism/

kinetics or toxicodynamics of the compound under assess-

ment are available. Non invasive in vitro techniques can

remedy this situation and provide at least metabolism

data from human cell lines and pathway-related UFs

could be appropriate in this case. This type of approach

could be applied to the analysis of human variability

in toxic effects based on mechanistic data; however, data

on pharmacodynamic variability are available in the

literature only for therapeutic effects (Hashemi et al.,

2002 unpublished; Renwick and Lazarus, 1998). In

an ideal world, physiologically based TK/TD models can

be generated to derive CSAFs (e.g. Gentry et al.,

2002; Meek et al., 2002; Nong et al., 2006; Chiu et al.,

2006).

The scientific community has also acknowledged the

use of probabilistic models to replace point estimates and

default assumptions (Slob and Pieters, 1998; Swartout

et al., 1998; Edler et al., 2002). More recently, predic-

tions of human variability in kinetics for compounds

handled by multiple pathways have been validated using

probabilistic Monte Carlo modelling and pathway-specific

variability (Dorne and Renwick, 2003).

Finally, the OMIC sciences (e.g. genomics, proteomics,

metabolomics, metabonomics) generate quantitative data

related to our understanding of toxic mechanisms at the

level of populations, individuals, cells and molecular

targets. Such applied research will provide a great source

of information to refine human and ecological risk assess-

ments, i.e. by revealing the similarities and differences

in toxicity mechanisms between individuals as well as

species. A recent research effort within the 6th frame-

work European project NOMIRACLE (Novel Methods

for Integrated Risk Assessment of Cumulative Stressors

in Europe http://viso.jrc.it/nomiracle/) identified several

options to harmonize the derivation of uncertainty factors

used in both human and ecological risk assessments

and the separation of uncertainty and variability using

mechanistic descriptors (Dorne et al., 2006).

Acknowledgements—This manuscript is dedicated to the memory of mymother Annie Gatel (1940–2000) and Yann Minard (1970–2006). Pro-fessor Kannan Krishnan is greatly acknowledged for his help to edit themanuscript of this review.

The author is grateful to the Department of Health, Health and SafetyExecutive (1998–2001 UK), Health Canada (2002–2004) and the Eu-ropean Commission (2004–2006 under the NO MIRACLE projectNumber 003956) for funding this work. The opinions reflected in thisreview are the author’s only.

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Human variability in hepatic and renal elimination: implications for risk assessment