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Perspectives
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Defining drug disposition
determinants: a pharmacogenetic–
pharmacokinetic strategy
David A. Katz, Bernard Murray, Anahita Bhathena and Leonardo Sahelijo
Abstract | In preclinical and early clinical drug development, information about the
factors influencing drug disposition is used to predict drug interaction potential,
estimate and understand population pharmacokinetic variability, and select
doses for clinical trials. However, both in vitro drug metabolism studies and
pharmaco­genetic association studies on human pharmacokinetic parameters have
focused on a limited subset of the proteins involved in drug disposition. Further­more,
there has been a one-way information flow, solely using results of in vitro studies
to select candidate genes for pharmacogenetic studies. Here, we propose a twoway pharmacogenetic–pharmacokinetic strategy that exploits the dramatic recent
expansion in knowledge of functional genetic variation in proteins that influence
drug disposition, and discuss how it could improve drug development.
Drug disposition is influenced by drug
metabolizing enzymes (DMEs), drug
transport proteins (DTPs), serum binding
proteins and transcription factors that
regulate DME and DTP expression (BOX 1).
Foreknowledge of the specific proteins
that influence the disposition of a new
chemical entity (NCE) is an important goal of
preclinical and early clinical drug develop‑
ment. Early availability of this information
enables mathematical modelling of the drug
inter­action potential of an NCE using
quantitative kinetic parameters of specific
DMEs1. This can therefore lead to better
projection of doses for subsequent studies.
Although tools exist to assess the role
of most of these proteins for the disposi‑
tion of an NCE, drug developers typically
learn only about a limited number of them
(that is, several cytochrome P450s (CYPs),
a few additional DMEs and the DTP
P‑glycoprotein), and generally do not know
the relative clinical importance of most of
the more than 170 drug disposition pathways
(TABLES 1,2; BOX 2; Supplementary
information S1 (table), S2 (table), S3 (table),
S4 (table)). Much of the knowledge about
drug disposition determinants comes from
academic laboratories after a drug is marketed.
Such knowledge has been highly beneficial for
patients; for example, when the prevalence of
severe adverse events in thiopurine S-methyl­
transferase (TPMT) poor metabolizers dosed
with thiopurines was identified2,3. However,
for many drugs, the pathways that determine
pharmacokinetic variation remain unknown
even years after regulatory approval. This
limits the ability of drug developers to identify,
manage and understand the consequences of
pharmacokinetic variability for efficacy, safety
and drug–drug interactions. For example,
statins were used by many millions of people
before it was discovered that their pharmaco­
kinetic variability, drug interactions, efficacy
and safety might be dependent on the DTP
solute carrier organic anion transport protein
1B1 (OATP1B1)4–6.
Association between a gene variant
and drug pharmacokinetics implies a
mechanistic role of the gene product in
drug disposition, and the potential outcome
of making such associations is to learn the
nature reviews | drug discovery
extent to which any of a wide range of
factors influences the disposition of a drug.
Increased knowledge and more accessible
technology should now make it easier for
drug developers to study which pathways
are responsible for the disposition of a
drug. In this article, after a brief overview
of the preclinical characterization of drug
disposition, we summarize current know­
ledge on pharmacogenetics and drug dispo­
sition. We then propose a new approach
in which pharmacogenetic results derived
from early clinical studies can both feed
back to additional targeted in vitro studies
and feed forward to optimize later-stage,
larger clinical trials for NCEs, contribute to
more informative drug labels, and thereby
potentially enable better drug use.
Preclinical drug disposition assessment
Preclinical characterization of drug dis­
position generally involves the assessment
of individual proteins for their role in the
disposition of the NCE. A summary of
currently available tools for such studies,
including purified or recombinant
proteins, selective substrates and inhibitors is
provided in Supplementary information S5
(table).
Although many purified or recombinant
DMEs — particularly members of the
human CYP, flavin monooxygenase (FMO),
monoamine oxidase (MAO), UDP glucu­
ronosyltransferase (UGT), sulphotransferase
(SULT), N-acetyltransferase (NAT) and
glutathione S-transferase (GST) families
— are commercially available, there are
substantial gaps in the availability of purified
or recombinant DMEs from other families.
Several DTPs have been functionally
expressed in recombinant cell-based assays,
but these are generally not commercially
available. When purified or recombinant
protein is not available, selective inhibitors
(if known and commercially available) can
be useful to deconstruct the disposition
process in perfused organs, tissue slices,
isolated cells or subcellular fractions.
However, sufficiently selective inhibitors that
distinguish between closely related proteins
have only been established for a few DME
or DTP families. Even among the CYPs, it is
still not possible to fully distinguish between
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Perspectives
Box 1 | Primer on drug disposition determinants
Four main types of drug disposition determinants are known, and these are outlined below.
Drug metabolizing enzymes
Drug metabolizing enzymes (DMEs) are proteins that catalyse the chemical transformation of
drugs and other small molecules. DMEs can be thought of as the body’s chemical defence
system (although there are certainly examples of DMEs converting a drug to a more toxic
metabolite), but also function in the synthesis and degradation of endogenous chemicals
(for example, neurotransmitters, nucleotides). Drugs are mainly metabolized in the liver and
intestines, but also in other organs.
Broadly, DMEs can be divided into two categories: functionalizing enzymes and conjugating
enzymes. Functionalizing enzymes introduce or reveal functional groups in a substrate through
oxidation (for example, cytochrome P450s), reduction (for example, aldo-keto reductases)
or hydrolysis (for example, epoxide hydrolases). Conjugating enzymes transfer moieties from
a cofactor to a substrate (for example, N‑acetyltransferases). Previously, functionalizing and
conjugating enzymes were referred to as catalysing ‘phase I’ and ‘phase II’ reactions.
This terminology is losing popularity, in part because it leads to the erroneous expectation that
the processes are ordered and sequential.
Drug transport proteins
Drug transport proteins (DTPs) facilitate the movement of drugs and other small molecules
across cellular membranes. The intestines, liver, kidney and brain are the organs where
facilitated drug transport most affects pharmacology, but DTPs are present in all tissues.
There are two main families of DTPs.
Members of the ATP-binding cassette (ABC) family utilize the energy of ATP hydrolysis for
the purpose of carrying small molecules across biological membranes, either against or with
concentration gradients. This process is known as active transport. Examples include
P‑glycoprotein and the multidrug resistance proteins.
Members of the solute-linked carrier (SLC) family enable the passage of small molecules across
biological membranes along concentration gradients. This process is known as facilitated
diffusion. Examples include organic ion transporters, organic anion transport proteins and
nucleoside transporters.
Serum binding proteins
Serum binding proteins often bind a large fraction of a drug that is in central circulation —
sometimes more than 99%. Protein binding affects pharmacology as only unbound (free) drug
can enter tissues. The main serum binding proteins are albumin and α‑1 acid glycoprotein.
Transcription factors
Transcription factors are proteins that regulate gene expression. When bound to a range of
exogenous chemicals, certain transcription factors induce the expression of DMEs and DTPs.
Pregnane X receptor and aryl hydrocarbon receptor are among the best-characterized
transcription factors that are relevant for drug disposition.
the four CYP3A enzymes, and selective
inhibitors for some CYPs (for example,
CYP2C18, CYP2G, CYP2R1, CYP2S1) have
not been identified. Indeed, little is known at
all about these and a number of other DMEs.
When a selective inhibitor is not available,
it may be possible to take advantage of
tissue-selective expression of particular
isoforms within a protein family to learn
the extent to which each can influence
NCE disposition (for example, the roles of
FMO1, FMO2 and FMO3 can be assessed
separately in kidney, lung and liver using the
same inhibitor). When neither recombinant
protein nor selective inhibitor for a DME or
DTP is available, observing that an NCE is
a competitive inhibitor (in perfused organs,
tissue slices, isolated cells or subcellular
fractions) towards a known selective sub‑
strate (again, if known and commercially
available) might indicate that a particular
DME or DTP is important for the NCE’s
disposition. If a drug induces DME or DTP
expression by a transcription factor binding
in a heterologous transcription activation assay,
it might autoinduce its own disposition as
well as that of other drugs.
To provide a comprehensive in vitro
survey of drug disposition determinants,
a laboratory needs the capability to per‑
form the diverse assay types mentioned
above, many of which must be developed
in-house. Because of the current lack of a
comprehensive toolset and the resources
required — and also because there has
not been a regulatory imperative for
additional investigation — a typical pre‑
clinical in vitro survey has covered only
several DMEs (mainly CYPs) and the DTP
P‑glycoprotein. Knowing whether these
factors can influence the disposition of
an NCE provides valuable but far from
294 | april 2008 | volume 7
comprehensive information to predict
drug interaction potential, estimate and
understand population pharmacokinetic
variability, and select doses for subsequent
clinical trials. Elucidation of the specific set
of disposition pathways that are important
for a particular NCE’s disposition has not
been achieved, mainly because the neces‑
sary resources have not been available.
There is a need for an improved toolset
to identify the most important proteins that
influence the disposition of an NCE. This
toolset should be more comprehensive in
coverage of DMEs, DTPs and other factors;
less diverse in assay types; feasible during
preclinical or early clinical development; and
affordable. We propose that a strategy based
on the growing knowledge of the influence
of pharmacogenetic factors on drug disposi‑
tion (summarized in the following section)
can help provide that toolset.
Pharmacogenetics and drug disposition
A genetic component of pharmacokinetic
variability was postulated more than 100
years ago by Archibald Garrod in studies of
patients with alkaptonuria7. Half a century
later, several drugs were shown to have
indistinguishable disposition in mono­
zygotic twins, but often distinct disposition
in dizygotic twins (for example, phenyl­
butazone8). These results established drug
disposition as a heritable trait. Deficiencies
of the DMEs NAT and butyrylcholin­
esterase (BCHE) were later identified as
risk factors for adverse effects of isoniazid9
and succinylcholine10, respectively, and the
genetic basis for these11–15 and other DME
poor metabolizer phenotypes (for example,
CYP2D616–20, TPMT21–23) were discovered
around 1990.
By the 1990s, there was an understand‑
able reticence in the pharmaceutical industry
to develop drugs that were substrates of
these few polymorphic DMEs because of the
likelihood of variable pharmacokinetic and
drug–drug interactions. However, there
were exceptions: atomoxetine, a sensitive
CYP2D6 substrate, was approved for use
in 2002 (REF. 24). Also in the 1990s, drug
developers began to utilize pharmacogenetic
studies to learn the magnitude of pharmaco­
kinetic variability of NCEs that could be
attributed to genetic variation. In general,
these studies focused on the few DMEs in
which there were known polymorphisms,
and were undertaken only when they were
considered necessary. That is, when in vitro
results indicated that the NCE was a
substrate of the polymorphic DME
(hypothesis-based experiments).
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Perspectives
Table 1 | Consistently replicated associations between genotype and clinical pharmacokinetics
Protein name (gene name)
Representative affected drugs
References‡
Alcohol dehydrogenase 2 (ADH2)
Ethanol
OMIM entry for ADH2 and
references therein
Alcohol dehydrogenase 4 (ADH4)
Ethanol
64
Aldehyde dehydrogenase 2 (ALDH2)
Ethanol
OMIM entry for ALDH2 and
references therein
Cytochrome P450 2A6 (CYP2A6)
Nicotine
65–70
Cytochrome P450 2B6 (CYP2B6)
Efavirenz, nelfinavir
71–73
Cytochrome P450 2C19 (CYP2C19)
Omeprazole, lansoprazole, pantoprazole, rabeprazole,
trimipramine, amitryptyline, imipramine, clomipramine
44,74,75 and references
therein
Cytochrome P450 2C9 (CYP2C9)
Phenytoin, warfarin, tolbutamide, glipizide, celecoxib, fluvastatin
76 and references therein
Cytochrome P450 2D6 (CYP2D6)
Imipramine, perphenazine, trimipramine, desipramine,
nortryptyline, clomipramine, tamoxifen
44 and references therein
Cytochrome P450 3A5 (CYP3A5)
Tacrolimus, saquinavir
77–84
Dihydropyrimidine dehydrogenase (DPYD)
5-Fluorouracil
OMIM entry for DPYD and
references therein
Flavin monooxygenase 3 (FMO3)
Sulindac
85,86
N-acetyltransferase 2 (NAT2)
Isoniazid, hydralazine, procainamide, dapsone, sulphamethazine
OMIM entry for NAT2 and
references therein
Organic anion transport protein 1B1*
(SLCO1B1)
Pravastatin, rosuvastatin
30–33
Pseudocholinesterase (BCHE)
Suxamethonium
OMIM entry for BCHE and
references therein
Thiopurine S-methyltransferase (TPMT)
6-Mercaptopurine, 6-thioguanine, azathioprine
2 and references therein
Uridine glucuronosyltransferase 1A1 (UGT1A1)
Irinotecan
87 and references therein
*Also known as OATP-C or OATP2. ‡Online Mendelian Inheritance in Man (OMIM) database web site: http://www.ncbi.nlm.nih.gov/sites/entrez?db=omim.
Until recently, limited knowledge about
functional genetic variation in DMEs (and
none about polymorphism in other drug
disposition factors) substantially limited
the scope of pharmacogenetics–pharmaco­
kinetics research. However, in the past few
years, substantial knowledge in this field
has been accumulated, and a review of the
literature reveals that there are now over
170 gene products known or expected to
have a role in drug disposition (BOX 3).
These include not only numerous DMEs
and DTPs, but also abundant serum binding
proteins and regulatory (transcription)
factors that control the expression of
DMEs and DTPs. More than half of the
corresponding genes are known to be poly‑
morphic (TABLES 1,2; BOX 2; Supplementary
information S1 (table)); most that are not
known to contain common functional poly‑
morphisms (Supplementary information S2
(table), S3 (table), S4 (table)) have not been
adequately studied to state with certainty
that they do not.
The 16 proteins involved in drug
disposition for which consistently
replicated associations between variants in
the corresponding genes and the human
pharmacokinetics of at least one drug have
been published are shown in TABLE 1. With
the exception of FMO3, for which only two
reports showing relationship to sulindac
pharmacokinetics were found, there were
three or more consistent reports for at least
one drug for each gene. FMO3 was included
in this group because the established associ‑
ation of variants in this gene with fish-odour
syndrome25 is additional evidence supporting
its relevance for xenobiotic disposition.
Nearly all the genes listed in TABLE 1 encode
DMEs, although there is also a gene that
encodes a DTP (OATP1B1). There is robust
scientific evidence showing that each gene
contains common variants (combined
minor allele frequency of variants sharing
a phenotype ≥5%), which have substantial
effects on human pharmacokinetics of one
or more drugs (see table 1 for references).
However, this does not mean that the rele­
vance of these variants for clinical practice
has been established; in fact, dose adjust‑
ments or contraindications based on only
CYP2D6, CYP2C9, TPMT and UGT1A1 are
currently included in US drug labels26.
TABLE 2 shows the 18 genes for which
common variants are likely to have a role in
nature reviews | drug discovery
drug disposition, having been shown to be
associated with the human pharmacokinetics
of one or more drugs, albeit in single studies.
Of these, 15 encode DMEs, 2 encode DTPs
and 1 encodes a serum binding protein
(α‑1 acid glycoprotein, gene = ORM1).
The preponderance of DMEs in TABLE 1 and
TABLE 2 reflects that DMEs were the sole
focus of pharmacogenetics research related
to drug disposition until recently. Many of
the studies cited in TABLE 2 are relatively
recent, and might therefore be replicated
in the near future. As this occurs, the range
of genes established as polymorphic deter‑
minants of human pharmacokinetics will
expand. In addition to pharmacokinetics,
several of these genes have also been
associated with drug efficacy or safety.
Common variants of at least 55 genes
encoding drug disposition factors have func‑
tional effects on protein activity or expression
(Supplementary information S1 (table);
BOX 2), but have not yet been associated with
human pharmacokinetics for any drug. It is
currently unknown how many of these genes
will be found to have meaningful influence
on human pharmacokinetics. Several of
them encode DMEs such as CYP3A4 and the
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© 2008 Nature Publishing Group
Perspectives
Table 2 | Associations between genotype and clinical pharmacokinetics*
Protein name (gene name)
α1-Acid glycoprotein (ORM1)
Alcohol dehydrogenase 3
(ADH3)
Breast cancer resistance protein
(ABCG2)
Catechol-O-methyltransferase
(COMT)
Cytidine deaminase (CDA)
Cytochrome P450 1A2 (CYP1A2)
Cytochrome P450 2C8 (CYP2C8)
Deoxycytidine kinase (DCK)
Epoxide hydrolase 1 (EPHX1)
Glutathione-S-transferase µ
(GSTM1)
Glutathione-S-transferase π
(GSTP1)
Glutathione-S-transferase θ
(GSTT1)
Inosine triphosphatase (ITPA)
Multidrug resistance-associated
protein 2 (ABCC2)
Nicotinamide-Nmethyltransferase (NNMT)
Uridine glucuronosyltransferase
1A6 (UGT1A6)
Uridine glucuronosyltransferase
1A9 (UGT1A9)
Uridine glucuronosyltransferase
2B15 (UGT2B15)
Notes
• Variants observed to be associated with the unbound serum fraction of quinidine
• Associations related to ethanol are controversial
• ADH3 is adjacent to ADH2 (Table 1) in the genome, so positive results for ADH3 may reflect
effects of ADH2
• ADH2 protein has a much lower Km for ethanol and has a greater role in its metabolism
• Variant observed to be associated with the pharmacokinetics of diflomotecan
• In one study, low activity variant was associated with low levodopa dose in patients with
Parkinson’s disease
• The same variant was not associated with levodopa pharmacokinetic parameters in a
similar study
• Earlier work associated levodopa to erythrocyte COMT enzyme activity but not to genotype
• Progression rate and survival of patients with cancer treated with gemcitabine have been
associated with CDA activity
• A CDA variant has been shown to reduce CDA activity in vitro
• One promoter variant has been associated with increased enzyme inducibility and another
with reduced theophylline clearance
• However, a CYP1A2 that can unequivocally be used to predict the metabolic phenotype in any
individual patient variant has yet to be identified
• Reduced function variants have been associated in single studies with reduced clearance of
r-ibuprofen and repaglinide
• The most common reduced-function variant is in strong linkage disequilibrium with
reduced-function variants of CYP2C9; this complicates interpretations of CYP2C8
pharmacogenetic results
• DCK deficiency may be important in AraC resistance; two promoter variants have been
associated with increased DCK expression and favourable response to therapy
• EPHX1 genotype has been associated with carbamazepine metabolic ratio
• Low epoxide hydrolase activity appears to be a risk factor for congenital malformations in
infants of mothers who take phenytoin; however, association of a specific genetic variant
has not been established
• Dual GST M1–T1 null (gene deletion) genotype was associated with increased risk for tacrine
hepatotoxicity
• Gene duplication (probably uncommon) affecting activity has been reported in a Saudi
population
• A variant has been shown to alter catalytic activity
• Associations with progression-free surivival in multiple myeloma and colorectal cancer,
susceptibility to therapy-related acute myelogenous leukaemia, and CNS relapse-free survival
in acute lymphocytic leukaemia, have each been reported in single studies
• Dual GST M1–T1 null (gene deletion) genotype was associated with increased risk for tacrine
hepatotoxicity
• Two research groups have observed an association of the 94A allele with adverse events of
azathioprine in patients with inflammatory bowel disease, but this finding was not replicated by
three other groups
• An ABCC2 genotype was associated with higher areas under the curve for irinotecan
and its metabolites
• An intronic variant was linked to plasma homocysteine levels
• Promoter and coding region variants are associated with reduced hepatic expression levels and
decreased enzymatic activity for most substrates
• An interaction of UGT1A6 variant genotype and use of aspirin/NSAID to lower risk for
colorectal adenoma has been observed in two studies
• In another study, the same UGT1A6 genotype was associated with increased colorectal cancer
risk; so, it is possible that the interaction is due either to genetic modulation of drug effect, or
drug modulation of a genetic role in disease
• An amino-acid change has reduced in vitro activity
• Several promoter polymorphisms have been identified; one that was associated with higher
transcription in a heterologous expression assay was not found to alter protein level or activity in
human livers, but was associated with efficacy and safety of combination irinotecan/capecitabine
• Protein level and activity in human livers was associated with two other promoter variants
(one higher, one lower)
• A variant has been associated with decreased metabolism of lorazepam and estradiol, and
with increased progression-free survival in patients with breast cancer treated with tamoxifen;
this last observation might be explained by effects on the drug or on oestrogen levels.
• A different non-synonymous SNP is common in Japanese individuals
References
88
89
90
91–93
94,95
96–100
101,102
103
104; OMIM‡
entry for EPHX1
and refs therein
105–107
108 and refs
therein; 109–114
107,115
116–120
121
122,123
124–131
40,132–136
137–141
*Consistent replication is so far lacking. ‡Online Mendelian Inheritance in Man (OMIM) database web site: http://www.ncbi.nlm.nih.gov/sites/entrez?db=omim. NSAID, non-steroidal
anti-inflammatory drug; SNP, single nucleotide polymorphism.
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Perspectives
UGTs, which are known to metabolize a wide
range of drugs27,28, and these may be among
those most likely to be found to be relevant
for human pharmacokinetic variability.
Finally, DMEs, DTPs and regulatory
factors for which the relevance of genetic
variation to drug disposition has not been
established are shown in Supplementary
information S2 (table), S3 (table) and S4
(table).
Proposal for a new PG–PK strategy
The increase in knowledge about phar‑
macogenetics and drug disposition sum‑
marized above — coupled with technology
advancements that have made genotyping
more affordable (with costs of less than
US$1 per genotype for multiple available
technologies) in the past decade — have
made broad application of pharmacogenet‑
ics in most drug development programmes
more feasible. Several platforms have been
developed to screen the known functional
variants that might influence drug disposi‑
tion in the context of early clinical trials
from which high-quality pharmacokinetic
data are also available.
The approach we propose includes a
broad evaluation of genotype–pharmaco­
kinetic relationships during early drug
development, together with in vitro studies,
to first generate and then confirm hypoth‑
eses about the pathways that are major
disposition determinants of an NCE.
Our core strategy for incorporation in NCE
development comprises five steps (FIG. 1).
The first step is to conduct in vitro
experiments before clinical trials to assess
whether, and to what extent, selected DMEs
and DTPs might influence disposition of
the NCE. This activity provides valuable
information about the potential role of
proteins that are known to influence the
disposition of many drugs and to mediate a
number of clinically important drug inter‑
actions, such as CYP3A and P‑glycoprotein.
These experiments have demonstrated
utility in drug development planning and
decision-making. Additional in vitro assays
may become standard on the basis of new
information about drug disposition and
interaction factors. For example, recent
results from our group29 and others30–33
have demonstrated that the hepatic uptake
DTP OATP1B1 (SLCO1B1) is a meaningful
determinant of drug disposition for statins
and other drugs, and that OATP1B1 inhibi‑
tors may have drug interaction potential.
Recombinant OATP1B1 can be expressed
in cell culture, and selective substrates and
inhibitors are available (Supplementary
Box 2 | Genes with common functional variants not yet associated with pharmacokinetics
Drug metabolizing enzymes
• Cytochrome P450 (CYP) family. CYP1A1, CYP1B1, CYP2A13, CYP2E1, CYP2G1, CYP2G2, CYP2J2,
CYP3A4, CYP3A7, CYP3A43, CYP4B1, CYP4F12.
• Other oxidoreductases. CBR3 (carbonyl reductase 3), FMO1 (flavin monooxygenase 1), FMO2,
MAOA (monoamine oxidase A), MAOB, PON1 (paraoxonase 1), NQO1 (quinone NADPH
dehydrogenase 1), NMOR2 (quinone NADPH dehydrogenase 2), ACADS (short chain acyl-coA
dehydrogenase).
• Glutathione‑S-transferase (GST) family. GSTA1, GSTA2, GSTM3, GSTO1, GSTO2, GSTZ1.
• Sulphotransferase (SULT) family. SULT1A1, SULT1A2, SULT2A1.
• Uridine glucuronosyltransferase (UGT) family. UGT1A3, UGT1A4, UGT1A7, UGT1A10, UGT2B7,
UGT2B17.
• Other transferases. HNMT (histamine‑N-methyltransferase); NAT1 (N‑acetyltransferase 1);
PNMT (phenylethanolamine‑N-methyltransferase).
• Hydrolases. GUSB (β‑glucuronidase), CES2 (carboxylesterase 2).
Drug transport proteins
• ATP binding cassette (ABC) proteins. ABCC3 (multidrug resistance-associated protein 3);
ABCC11 (multidrug resistance-associated protein 8).
• Solute-linked carrier (SLC) proteins.SLC28A1 (concentrative nucleoside transporter 1);
SLC29A1 (equilibrative nucleoside transporter 1); SLC15A2 (oligopeptide transporter 2;
also known as PEPT2); SLCO1A2 (organic anion transport protein 1A2; also known as OATP-A);
SLCO2B1 (organic anion transport protein 2B1; also known as OATP-B); SLC22A12 (organic
anion transporter 4‑like; also known as URAT1); SLC22A1 (organic cation transporter 1);
SLC22A4 (organic cation transporter N1); SLC22A5 (organic cation transporter N2); SLC10A1
(sodium-taurocholate cotransporting polypeptide).
• Nuclear receptors. AHR (aryl hydrocarbon receptor); PXR (pregnane X receptor; also known
as NR1I2).
Additional information and literature references for each protein are provided in Supplementary
information S1 (table).
information S5 (table)). Learning whether
an NCE is an OATP1B1 substrate or inhibi‑
tor could become an ordinary preclinical
activity.
The second step is to conduct a broad
search for associations of pharmacokinetics
with genotypes during the first-in-human
study (BOX 4). The scope of this search could
include all relevant genes for which there
is reasonable expectation that a positive
result can be obtained (based on study
power) and readily interpreted based on
current information about functional
variants. This includes genes for which there
are well-established (TABLE 1) or observed
(TABLE 2) associations between genetic vari‑
ants and human pharmacokinetics, as well
as genes for which in vitro evidence indicates
that common variants alter the activity
or expression of the gene product (BOX 2;
Supplementary information S1 (table)).
Defining the search scope in this way will
include over half of known or suspected
drug disposition determinants. For genes in
which common variants are known but their
function is not (Supplementary information
S2 (table)), associations between genotype
nature reviews | drug discovery
and human pharmacokinetics might not
be readily interpreted without subsequent
experimentation to define the phenotype of
the associated variant(s). These genes might
be screened if there is preclinical evidence
that the NCE is a substrate or inhibitor,
and perhaps not otherwise. Because firstin-human studies are generally not large,
the likelihood of finding an association
between an uncommon functional variant
(Supplementary information S3 (table))
and human pharmacokinetics is low. If
there is preclinical evidence that the NCE
is a substrate or inhibitor, a special study to
determine the clinical relevance of genotype
therein may be a better approach to assess
the effect of uncommon functional variants
than the general strategy described here.
Genes for which there is no published
information regarding common genetic
variation (Supplementary information S3
(table)) might be screened using single
nucleotide polymorphisms (SNPs) found
in online databases. Including these SNPs
would enable some assessment of essentially
every known or expected drug disposition
determinant.
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Perspectives
Box 3 | Survey of genetic variants in drug disposition factors
A search of the scientific literature and online resources was conducted to provide an overview of
the variation in genes that encode drug metabolizing enzymes (DMEs), the drug transport proteins
(DTPs), abundant plasma binding proteins, and factors that regulate DME and DTP expression.
For each gene, at least the following sources were searched:
•Online Mendelian Inheritance in Man (OMIM) (http://www.ncbi.nlm.nih.gov/entrez/query.
fcgi?db=OMIM)
•Medline (accessed through Dialog DataStar) was searched using the OMIM gene symbol.
If this simple search yielded >100 articles, it was limited by applying the condition AND
(pharmacogenetics OR polymorphism-genetic).
•The Pharmacogenetics and Pharmacogenomics Knowledge Base (http://www.pharmgkb.org)
Genes were categorized as follows:
•Consistently replicated association of variants with human pharmacokinetics of at least one drug
(TABLE 1).
•Association of variants with human pharmacokinetics of one or more drugs, but without
consistent replication for any drug (TABLE 2).
•Functionality of common variants (≥5% combined frequency of variants of similar phenotype)
demonstrated by in vitro methods, but no published association with human pharmacokinetics
(BOX 2; see Supplementary information S1 (table)).
•Common variants have been published, but functionality or association with human
pharmacokinetics have not (see Supplementary information S2 (table)).
•Functionality of one or more rare (or unreported frequency) variants demonstrated (see
Supplementary information S3 (table)).
•No information on variant functionality or association related to human pharmacokinetics;
rare mutations in some of these genes have been linked to Mendelian metabolic disorders (see
Supplementary information S4 (table)).
The aim of this overview is to provide a sense of the diversity of pharmacogenetics–
pharmacokinetics knowledge. Although extensive, the tables are not necessarily comprehensive.
We generally did not include reports of association unrelated to the pharmacokinetics of a
specific drug, even when a phenotype (such as cancer susceptibility) might be related to
xenobiotic disposition. Literature citations are either to our choice of review articles or
to primary literature supporting what we considered to be the most important sort of information
available. For example, if variants in a gene were associated with human pharmacokinetics in a
clinical study we do not also cite work showing the molecular phenotype of those variants; if the
phenotype of common variants in a gene is understood we do not also cite work concerning the
phenotype of rare variants. We apologize in advance to any scientists whose relevant
publications are not cited.
The third step is to replicate any observed
associations during the multiple rising-dose
study (BOX 4). As large numbers of genes may
be screened, replication of any observed
association from a first-in-human study is
essential to minimize the risk of making a
decision based on false-positive results.
The fourth step is to confirm the gene
product’s role using an in vitro assay (if the
role was not already known from preclinical
work) before or concurrently with Phase II
clinical studies (BOX 4). When functional
genetic variants affect protein activity
(rather than expression), the activities of
those variants towards the NCE should also
be measured in the in vitro assay, as some
variants have substrate-dependent effects.
For example, the *17 allele of CYP2D6 has
normal catalytic activity towards codeine but
reduced activity towards dextromethorphan
and debrisoquin34. If different variants in
the same gene were used jointly to classify
individuals (for example, into poor metabo‑
lizer or non-poor metabolizer) for clinical
pharmacogenetic–pharmacokinetic associa‑
tions, the classification should be confirmed
by showing that the different variants share
a similar phenotype toward the compound
(for example, SLCO1B1 and atrasentan29).
The fifth step is to estimate the magni­
tude of genotype effect in a population
pharmacokinetics model during Phase II
clinical studies. At this point, genotype is
used as a covariate in the population phar‑
macokinetics model, just as sex or weight
might be used. It is often not feasible to use
genotype as a population pharmacokinetics
model covariate before Phase II, because
the number of subjects in Phase I clinical
studies (BOX 4) is generally not large enough
to ensure an accurate estimate of the magni‑
tude of genetic effect in a diverse population.
298 | april 2008 | volume 7
Sufficient numbers of individuals having
rare genotypes might not be dosed with an
NCE until Phase III clinical studies (BOX 4),
or it might be necessary to conduct an
enriched clinical study to appropriately
inform the model.
Knowledge about genetic variability rele­
vant to drug disposition can be applied in
several ways, and these are discussed in the
following section.
Applications of PG–PK knowledge
Potent inhibitors or inducers of a polymor‑
phic DME or DTP are likely to have effects
that are proportional to the magnitude of
effect of genotype (for example, CYP2C19
and ticlopidine35). Thus, associations
between human pharmacokinetics of an
NCE and genetic variation identifies a
potential pathway for drug–drug inter­
actions, and hence the need for specific and
additional drug–drug interaction studies.
By contrast, little or no effect of a wellestablished genetic variant might show that
certain drug–drug interaction studies are
not necessary. For example, a CYP2D6 inhib‑
itor is unlikely to have a meaningful effect
on a CYP2D6 substrate if that substrate’s
pharmacokinetics are not meaningfully
influenced by CYP2D6 genotype. Prioritizing
drug–drug interaction studies on the basis of
clinical pharmacogenetic effects will improve
on the current approach of doing so on the
basis of in vitro experiments alone1.
CYP2D6 can be used to exemplify
another application of clinical pharmaco­
genetics: to increase confidence that
pharmacokinetic outliers are not likely to
be an issue in later development. Lack of a
significant effect of any genotype suggests
that multiple distribution pathways are
contributing to drug disposition equally, and
that genetic pharmacokinetic outliers are at
most very rare (as they would have to carry
multiple rare genotypes). Pharmacogenetic–
pharmacokinetic relationships can help to
distinguish between ordinary population
variability and true outliers in a limited
dataset — a relevant factor in decisions of
how (or whether) to move a programme
forward. For example, in one study we iden‑
tified that unusual pharmacokinetics of a
CYP2D6 substrate was observed in a person
whose CYP2D6 genotype had a population
frequency of <0.4% and was thus reasonably
considered an outlier36. In other situations,
learning that one or more apparent outliers
share genetic constitution with other subjects
suggests a greater level of inter-individual
variation rather than the existence of two
separate populations.
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Perspectives
Dose verification and
proof of concept
Assess safety and tolerability
in healthy volunteers
Preclinical
development
Study metabolism
of NCE by selected
DMEs and DTPs
Phase I
First in human
Long-term safety
and efficacy
Phase II
Phase III
Multiple rising dose
Broad search for associations
of PK with genotype (include
all PK-relevant genes)
Replication
of observed
associations
Confirm the role of gene
product in metabolism of NCE;
measure activity of variants
Build population PK model
to estimate magnitude of
genotype effect
Use information for
follow-up compounds
Figure 1 | Flow chart of the proposed pharmacogenetic–pharmaco­
kinetic strategy. In vitro experiments are conducted before clinical trials
to assess whether, and to what extent, selected drug metabolizing
enzymes (DMEs) and drug transport proteins (DTPs) might influence the
disposition of a new chemical entity (NCE). Then, during the first-in-human
study, a broad search for associations of pharmacokinetic (PK) properties
The use of genotype–pharmacokinetic
associations can also enhance the design
of special population or regional bridging
studies (BOX 4). If a drug’s pharmacokinetic
properties are sensitive to a polymorphism
in a gene, success in special population
studies may depend on including that gene
in the design. How this is most efficiently
done may vary between situations, depend‑
ing in part on the strength of the effect and
frequency of the genotype. For some studies,
retrospective determination of whether an
unbalanced representation of genotypes
contributed to group differences may be
sufficient. Sometimes, recruitment of geno‑
type-balanced cohorts or separate matched
cohorts of different genotypes, or excluding
individuals of a certain genotype, may be
desirable to address the key clinical phar‑
macology question. These approaches can
be particularly relevant in regional bridging
studies, as genetic variant frequencies are
known to differ substantially between
populations of different geographic origins
(for example, CYP2C9 (REF. 37), CYP2C19
(REF. 38), CYP2A6 (REF. 39), UGT1A1 and
UGT1A9 (REF. 40), NAT2 (REF. 41), OATP1B1
(REF. 42)).
For example, suppose that pharmaco­
genetics–pharmacokinetics research in both
first-in-human and multiple rising-dose
studies showed that, on average, individu‑
als heterozygous for a low activity allele
of CYP2A6 (intermediate metabolizers)
had higher levels of an NCE than those
homozygous for the wild-type allele of the
gene (extensive metabolizers). Furthermore,
with genotypes is conducted. Any observed associations are replicated
during the multiple rising-dose study. Before or concurrently with Phase II
Nature Reviews
| Drug
Discovery
clinical studies, the gene product’s role is confirmed
using an
in vitro
assay
(if the role was not already known from preclinical work). The magnitude of
genotype effect in a population pharmacokinetics model is then estimated
during Phase II clinical studies.
in vitro experiments conducted subsequently
showed that the NCE is a CYP2A6 substrate
and population pharmacokinetic analysis
of Phase II clinical trial results confirm the
influence of the CYP2A6 genotype on the
pharmacokinetic properties of the NCE.
To facilitate global development of the NCE,
a pharmacokinetic bridging study between
Japanese and Caucasians is a next step2.
Successful conduct of this study could elimi‑
nate the need for a separate full development
programme in Japan43. Yet, because of highly
different variant frequencies, individuals
homozygous for low activity alleles (poor
metabolizers) are common among Japanese
but not in Caucasians. Hence, random
recruitment of Japanese and Caucasians
(the standard practice for regional bridging
studies) is almost certain to fail to show
equivalent pharmacokinetics between the two
groups. There are several possible regional
bridging trial designs that might provide the
evidence to avert a requirement to conduct
similar full development programmes in
both groups, including recruitment of
genotype-matched cohorts of Japanese and
Caucasians. However, there is currently a
lack of public experience, and hence uncer‑
tainty, as to whether this or other designs
will be acceptable to regulatory agencies.
Another way to use this information is
in dose selection for pivotal studies (BOX 4).
Understanding that the dose–exposure
relationship differs between identifiable
groups may lead to a decision to move
forward with a dose or doses that differ from
what might have been selected considering
nature reviews | drug discovery
a homogeneous population. For example,
it may be desirable to increase the pivotal
study dose to enhance efficacy among
individuals who can be expected to have
lower exposures (FIG. 2). The drug levels of
some leading antidepressants (for example,
paroxetine, venlafaxine) or antipsychotics
(for example, olanzapine, aripiprazole)
are moderately influenced by CYP2D6
genotype44. These drugs are generally safe
at a range of doses45–48; however, there
have been reports of poor efficacy related
to low drug levels in CYP2D6 ultrarapid
metabolizers49–51. FIGURE 2a illustrates how
pharmacogenetics might have been applied
during the development of these drugs.
Here, the ‘default’ dose represents the lowest
dose that showed efficacy in pivotal studies
and is generally the recommended starting
dose in the drug’s labels. Suppose that the
association between CYP2D6 genotype and
pharmacokinetics had been established
during Phase I and II clinical studies. Then,
the developers of these drugs could have
predicted that using a somewhat higher
dose (the pharmacogenetics-based dose) in
pivotal studies would improve the efficacy
profile in ultrarapid metabolizers and not
meaningfully diminish the safety profile in
other groups (including poor metabolizers).
Perhaps they would have selected the phar‑
macogenetics-based doses for their pivotal
studies. Because the drug label indicates
doses studied in controlled efficacy trials,
such a decision would have been reflected
in the label and perhaps improved clinical
practice using these drugs.
volume 7 | april 2008 | 299
© 2008 Nature Publishing Group
Perspectives
Box 4 | Phases of clinical development and clinical study types
The clinical trials that lead to approval of a new chemical entity (NCE) as a drug are typically
divided into three phases, each of which commonly comprises several individual clinical trials.
While the distinctions are not absolute, the goals of each phase and some of the types of clinical
trials typically considered as part of each phase are described below.
Phase I. Phase I drug development includes clinical studies in which an NCE is dosed in a small
group of (healthy) volunteers (usually <150). It includes first-in-human and multiple rising-dose
(MRD) studies. The main goal of Phase I studies is the assessment of the compound’s safety,
tolerability (dose-limiting toxicity, maximum tolerated dose) and pharmacokinetics. Other goals
such as early assessment of efficacy (generally using biomarkers) or clinical pharmacology issues
(such as food effect) may also be included.
First in human. In a first-in-human study, healthy volunteers (patients in some therapeutic areas)
are given single doses of an NCE. Generally, a low dose is administered to the first group of
subjects and the dose is progressively increased until a maximum tolerated dose is reached, or
the highest intended dose is given without adverse effects. The purpose of this study is to begin
learning the pharmacokinetics, safety and tolerability of an NCE.
Multiple rising dose. A multiple rising-dose study is generally conducted in the same manner
as a first-in-human study, except that multiple doses of an NCE (or placebo) are given to each
subject. The purposes of multiple rising-dose studies are to further characterize
pharmacokinetics, safety and tolerability; obtain dosage and administration information for
Phase II studies; and frequently to investigate concentration– or dose–response relationships
using biomarkers.
Phase II. Phase II drug development includes clinical studies aimed towards dose verification
and optimization in a defined population of patients (usually <500). Studies to identify unique
or major metabolites, understand drug interaction liabilities and observe the NCE’s safety in
special populations may also be considered part of this phase.
Proof of concept. A proof-of-concept study is typically the first in which patients, rather than
healthy volunteers, are enrolled. This study provides the first evaluation of clinical efficacy for
an NCE. If the pharmacology is unprecedented, this study also provides the first evidence
for clinical efficacy based on the pharmacological target.
Dose-ranging. In a dose-ranging study, the efficacy and safety of several doses or regimens of an
NCE are compared in patients. Results from a dose-ranging study are used to choose the dose(s)
for pivotal studies.
Phase III. Phase III drug development is the most costly part. This is when hundreds or thousands
of patients are studied in controlled, multicentre and often multinational trials to assess longterm safety and efficacy of the drug (benefit–risk ratio). The usual comparator is the established
standard of care, and study end points must be of clinical relevance.
Pivotal study. A pivotal study is an essential part of the regulatory submission package that is
meant to demonstrate the safety and efficacy of the NCE in a broad patient population.
The results of pivotal studies typically determine the indications for which a new drug is labelled.
Special population study. A special population study assesses an NCE’s pharmacokinetics,
efficacy or safety in a group that may be excluded from pivotal studies (for example, hepatically
impaired, paediatric subjects).
Bridging study. A bridging study is performed to allow extrapolation of clinical data from one
geographical region (for example, North America/Europe) to the population in a different
geographical region (for example, East Asia). The goal of a bridging study is to demonstrate that
an NCE has similar characteristics, often pharmacokinetics, in groups of subjects drawn from
each of the regions.
In another situation, if a drug’s efficacy
continues to increase as the dose approaches
the amount that is the maximally tolerated
dose, it may be desirable to sacrifice some
efficacy across the population by lowering
the pivotal study dose in order to be able to
safely dose the entire patient population,
including those who can be expected to have
higher exposures based on their genetic
constitutions (FIG. 2b).
Additionally, pharmacogenetics–
pharmacokinetics research during Phase I
and IIa drug development might reveal
dose–exposure relationships that are so
different between genotype groups that
there is no single dose of an NCE that can be
predicted to be generally safe and effective
for the entire patient population (FIG. 2c).
If clinicians would not be likely to accept
required pre-prescription testing to select
300 | april 2008 | volume 7
a dose for an individual, this information
might be used to cease development of an
NCE before larger investment in Phase IIb
and III studies. Moreover, identifying a
specific gene product that is related to an
unacceptable pharmacokinetic profile
provides the opportunity to enhance the
probability of future success by screening
against this gene product before advanc‑
ing chemically or mechanistically similar
follow-up compounds into clinical trials.
However, if unmet medical need allows
for pairing of a drug and a genetic test, the
pharmacogenetic results might provide
a path forward in which pivotal studies
would be conducted using different doses
for each genotype group. Thiopurine drugs
(for example, azathioprine) are effective for
treatment of certain leukaemias, yet can
lead to life-threatening adverse events52.
Thiopurine drug levels are strongly influ‑
enced by TPMT genotype53,54. Suppose that
this association had been established during
Phase I and II clinical studies of azathio‑
prine. Then, different doses could have been
tested for extensive, intermediate and poor
metabolizer groups. Instead of the current
situation, in which extensive metabolizers
are probably under-dosed and physicians are
left to their best judgment to select doses for
individual patients, azathioprine might have
been developed in a way that its label could
recommend specific doses (or dose ranges)
according to TPMT genotype.
Early clinical pharmacogenetics data
might also identify rare genotypes that should
be studied during later development. Because
of the limited number of subjects in Phase I/
IIa trials, less frequent genotypes might not
be represented. Even when there is a numeri‑
cal but not clinically meaningful difference
between wild-type and heterozygous geno‑
type groups, a special study that enrols only
healthy volunteers or patients with the variant
genotype, or in which the study cohort is
deliberately enriched with individuals with
the variant genotype, may be justified. This
might apply when a drug is being tested in
global clinical trials that are likely to include
variant genotypes that were uncommon in
the Phase I population but are more common
among other geographical ancestries.
As described above, in the context
of NCE development only incremental
resource commitment is necessary to reap
the potential benefits of pharmacogenetics–
pharmacokinetics research. The interest
of regulatory agencies in this research26
provides additional incentive for the pharma‑
ceutical industry to make such investments.
The approach described herein could also be
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© 2008 Nature Publishing Group
Perspectives
a Minimum
for efficacy
b
c
Maximum
for safety
Population
Dose
Unselected
Genotype 1
Genotype 2
Default
Unselected
Genotype 1
Genotype 2
PG-based
Unselected
Genotype 1
Genotype 2
Default
Unselected
Genotype 1
Genotype 2
PG-based
Genotype 1
Genotype 2
Figure 2 | Scenarios in which pharmacogenetic information related to pharmacokinetics in
early drug development can support decision-making for later development. Each horizontal
Nature
Reviews
| Drug Discovery
line represents a predicted range of exposures for a given dose, with
red lines
representing
doses
that extend outside the clinical window for safety and efficacy. In (a) and (b), the predicted range of
concentrations for the total patient population is misestimated if patients with genotype 2 (probably
a relatively small percentage) are not modelled separately. In both of these scenarios, pharmaco­
genetic (PG) information enables dose adjustment to enhance the probability of success of subsequent
studies. In (c), separate doses for genotypes 1 and 2 are necessary for the successful development of
a drug.
applied to further characterize the disposi‑
tion pathways of currently available drugs.
It is not clear how this research would be
funded, particularly for generic and overthe-counter drugs. The large investments
to conduct well-controlled clinical studies
are difficult to justify solely for pharmaco­
genetics purposes. The US National
Institutes of Health and the Food and Drug
Administration are funding clinical trials
for some drugs (for example, irinotecan,
tamoxifen, warfarin), yet additional funding
from government and other sources are
needed to fully recognize the potential
of pharmacogenetics–pharmacokinetics
research for already marketed drugs.
Limitations of the strategy
The exploratory pharmacogenetic–
pharmaco­kinetic strategy we have outlined
can readily be applied today for many but
not all genes that are relevant for drug
disposition. For some genes, the functional
variants will typically be observed only
in heterozygous individuals and so only
particularly strong pharmacokinetic pheno‑
types are likely to be detected during early
clinical development. For example, if nine
individuals (15%) of a Phase I cohort of 60
individuals are heterozygous for a certain
genotype, there is 80% power (assuming
a variance of one in each genotype group)
to detect a twofold difference between the
average clearance of the wild-type and
heterozygous groups. These numbers are
adequate, as the goal is to make decisions
based on large effects of genotype on
pharmacokinetics.
The strategy is most readily applied
for genes in TABLES 1,2; BOX 2 and in
Supplementary information S1 (table),
as functional variants are sufficiently
understood to enable interpretation
of either positive or negative results of
clinical pharmacogenetic–pharmacokinetic
associations. For many potentially phar‑
macokinetic-relevant genes, the molecular
or cellular phenotypes of variants have not
been reported (Supplementary information
S2 (table), S4 (table)). Without phenotype
information, interpretation of clinical phar‑
macogenetic–pharmacokinetic associations
is tenuous. Many drug developers might not
wish to perform exploratory clinical phar‑
macogenetics research for these genes until
variant phenotype information is publicly
available. Research to establish the pheno‑
types of common variants in these genes
will enable a more comprehensive applica‑
tion of pharmacogenetics–pharmacokinetics
nature reviews | drug discovery
research in drug development. For other
pharmacokinetic-relevant genes, the only
known functional variants are uncommon
(Supplementary information S3 (table)).
Exploratory pharmacogenetic–pharmaco­
kinetic association analysis during Phase I
clinical studies usually will not have
adequate power to assess the role of these
in an NCE’s disposition. A more effective
approach may be to conduct an additional
pharmacokinetics study recruiting subjects
having the uncommon genotype(s), when
in vitro experiments indicate that the
NCE is a substrate. There will be some
genes for which exploratory pharmaco­
genetic–pharmacokinetic association
studies will probably never be useful.
This might already be the situation for the
gene encoding P‑glycoprotein (ABCB1);
associations of common ABCB1 variants
to any drug disposition phenotype have
been inconsistently replicated or are even
contradictory26,55, suggesting that the known
common genetic variants may be nonfunctional. The role of this important DTP
in the disposition of new drugs may need
to continue to be established by in vitro and
drug–drug interaction studies.
Some may have a perception that
substantial changes to early development
processes, trial design or resource invest‑
ment would be necessary to implement this
strategy. This is not the case. Indeed, one
impetus for a drug developer to adopt it is
as an alternative to a larger resource invest‑
ment to build a wider range of in vitro tech‑
nologies for preclinical evaluation of diverse
drug disposition determinants. Our strategy
has as its first step the same preclinical analy­
sis of an NCE that is ordinary in today’s
pharmaceutical industry drug metabolism
laboratory. To help confirm observed phar‑
macogenetic–pharmacokinetic associations,
it may sometimes be necessary to develop
in-house capability to perform specific
assays related to the pathway that appears
meaningful for an NCE’s disposition.
Alternatively, a drug developer may choose
to collaborate with an academic laboratory
that has the appropriate assays, as we did
in our work related to the role of OATP1B1
in the disposition of atrasentan29. The same
genotyping technologies can be applied to
any gene, so investment in a single platform
is sufficient to enable the genetic experi‑
ments. Several vendors provide high-quality
reagents and equipment to generate all of
the relevant genetic data. In addition, several
service providers are available for those
drug developers who do not have or wish to
build in-house genotyping capability.
volume 7 | april 2008 | 301
© 2008 Nature Publishing Group
Perspectives
Glossary
Alternating group design
An alternating group design is similar to an escalating-dose
design, except that each group of subjects participates in
several study periods. The same subjects in each group
receive placebo in every period, but the dose of study
drug escalates at each subsequent period.
Crossover design
In a crossover design, each group of subjects is randomized
to receive all treatments (including placebo), usually
separated by a washout period to eliminate carry-over
effects between treatments. A crossover design is generally
considered to provide increased sensitivity to treatment
effects as it virtually eliminates inter-individual variability
(each study subject functions as their own control), and hence requires fewer participants than a parallel arm
design. However, the informativeness of pharmacogenetic
analysis is minimized in this study design.
drug transport protein. This classification applies to a
specific enzyme or protein; a single individual can be an
extensive metabolizer for one enzyme and not for another.
Fish-odour syndrome
This is an inborn error of metabolism accompanied by
fish-like body odour. The offensive odour is due to the
build-up of amino-trimethylamine (TMA) derived from
foodstuffs such as egg yolk, liver, kidney, legumes, soy
beans, peas and saltwater fishes. TMA is normally oxidized
by the drug metabolizing enzyme flavin monooxygenase 3
(FMO3); genetic deficiency in this enzyme is the
syndrome’s cause.
Functional genetic variants
New chemical entity
(NCE). This is a common term in the pharmaceutical
industry for a chemical that is being tested to learn
whether it is useful as a drug. Generally (and in this paper)
NCE refers only to small molecules and not to large
molecules such as peptides, proteins and nucleic acids that also may be developed as drugs.
Pharmacogenetics
(PG). This is the study of how genetic variation influences
drug response. In this paper, we focus on the subset of PG that relates genetic and pharmacokinetic variation.
Pharmacokinetics
These are variant gene sequences that alter the
expression, activity or substrate specificity of the
corresponding protein.
(PK). Is the study of how the body affects drugs. In this
paper, we frequently talk about PK variation (or variability),
which refers to differences between individuals of the
concentration–time profile of a drug.
Drug disposition
Heterologous transcription activation assays
Polymorphic
Drug disposition is what happens to a chemical after it
enters the body. After oral dosing, drug disposition
includes absorption from the gastrointestinal tract into
central circulation; distribution to and between various
tissues; metabolism to different chemicals; and excretion
(usually in urine or faeces).
These are experimental tools used to measure the
interaction of a compound with nuclear receptors. These
can either be cell-based or cell-free systems engineered so
that a readily detectable product (RNA or protein) is made
in larger amounts when a ligand for the nuclear receptor is
present.
In the context of pharmacogenetics, polymorphic refers to when a DNA sequence exists in two or more forms in
human populations. Polymorphism refers to the DNA
sequence that is polymorphic.
Escalating-dose design
Intermediate metabolizers/intermediate
transporters
In an escalating-dose design, a first group of subjects is
randomized to either the lowest dose of study drug or
matching placebo, and subsequent groups (composed of
different subjects) are each randomized to the next higher
dose of study drug or matching placebo.
Poor metabolizers/poor transporters
These are individuals who have very low or no activity of a drug metabolizing enzyme or drug transport protein,
respectively. Poor-metabolizer alleles include those that
lead to premature termination of the protein, and gene
deletions.
These are individuals who are homozygous for a wild-type
(normal activity) form of a drug metabolizing enzyme or
These are individuals who have reduced activity of a drug
metabolizing enzyme or drug transport protein. Reduced
activity may result from a structural change in the enzyme
or a lower level of protein expression. An intermediate
phenotype can result either from heterozygosity (for
example, one extensive metabolizer allele + one poor
metabolizer allele) or homozygosity (for example, for a
reduced expression allele).
These are individuals who have unusually high activity of a
drug metabolizing enzyme. Most commonly this is a result
of gene duplication. We are not aware of an ultrarapid
transporter phenotype.
We expect that discovery of pharmaco‑
genetic–pharmacokinetic associations will
generally be done as piggy-back activities
to Phase I and II clinical studies that are
conducted as they are today. Collecting DNA
samples with appropriate informed consent
from every subject in Phase I clinical studies
is already an ordinary activity at many large
pharmaceutical companies, and most at least
incorporate optional consent for pharmaco­
genetics–pharmacokinetics research into
Phase II clinical studies (William, J. A. et al.,
personal communication). Both DNA sample
collection and genotyping are inexpensive in
the context of clinical trial costs (William,
J. A. et al., personal communication).
Because sample sizes might be increased in
combined analyses using samples and data
from first in human or multiple rising dose
and other Phase I studies, drug developers
should ensure that inclusion and exclusion
criteria are consistent across all Phase I studies
of an NCE. This is also already normal
practice at major pharmaceutical companies.
So, there are few operational hurdles to
implementing our proposed pharmaco­
genetic strategy in drug development.
However, an ongoing trend in the phar‑
maceutical industry that may limit the future
application of our pharmacogenetic strategy
is a move away from the traditional firstin-human and multiple rising-dose Phase I
clinical studies with escalating dose design.
Alternatives, including crossover designs
and alternating group designs, are growing in
popularity. The attractions of these designs
are reduced cost, exposing fewer healthy
volunteers (who can gain no health benefit
from the study) to potential health risk, and
reduced inter-individual variation (which
further reduces the necessary number of
subjects in the study). While crossover and
alternating group designs are more efficient
for assessing certain safety parameters
(for example, QT prolongation, chronotropic
effects) of an NCE than the escalating dose
design, the trade-off is that they provide less
information about population variability in
pharmacokinetics and adverse event profile
of an NCE. The power of a pharmacogenetic
study rests on the degree of pharmacokinetic
variability in the study population. Hence,
the growing popularity of smaller first-inhuman and multiple rising-dose
studies in the pharmaceutical industry
potentially makes our strategy more difficult
to implement during the development of
an NCE. If sufficient subjects are not dosed
during Phase I trials, it may be necessary to
use Phase II trials to both replicate genetic
associations as well as quantitate the mag‑
nitude of genotype effect in a population
pharmacokinetics model.
One of the potential applications of our
proposed strategy is prediction of an NCE’s
drug interaction potential through any
of a wide range of pathways (see below).
However, clinical drug-interaction models
are not available for most disposition path‑
ways. This is a key technology development
need. There are few widely accepted, specific
probe drugs for use in drug interaction clini‑
cal studies56. Although selective substrates
and inhibitors of many pharmacokinetic
relevant factors are established for use in vitro
(Supplementary information S1 (table)),
many of these compounds are not (or have
not been shown to be) safe for human use.
Furthermore, probe drugs currently con‑
sidered as gold standards for certain types of
drug interactions might not have the degree
Extensive metabolizers/extensive transporters
302 | april 2008 | volume 7
Ultrarapid metabolizers
www.nature.com/reviews/drugdisc
© 2008 Nature Publishing Group
Perspectives
of in vivo specificity as commonly believed.
For example, rifampin (also known as
rifampicin) is considered a standard probe
to study the effect of inducing several DMEs
and DTPs on the pharmacokinetics of an
NCE56. When an NCE is given after several
days of rifampin dosing, there have been
two expected outcomes. If rifampin induces
a DME that is important for the NCE’s
metabolism, or a DTP that is important
for limiting its absorption or increasing
its excretion, NCE levels will be decreased
(relative to levels before rifampin dosing).
Otherwise, the levels should be similar
before and after rifampin dosing. Yet, we
recently observed that rifampin had the
unexpected effect of increasing atrasentan
concentrations57. Pharmacogenetics–
pharmaco­kinetics research, following the
strategy proposed herein, established that
this occurred via inhibition of the hepatic
uptake DTP OATP1B1 (REF. 29). Several
other drug–drug interactions have also
recently been recognized as mediated by
OATP1B1 (REFS 58–63). As the range of
DMEs, DTPs and other factors known to
mediate drug interactions grows beyond the
few that are known today, we expect that
the dual effect of rifampin will not be the
only surprise from widely accepted clinical
‘victims’ or ‘perpetrators’ of drug–drug
interactions. Our work showing that the
effect of rifampin on atrasentan levels are
mediated via OATP1B1 exemplifies that using
clinical pharmacogenetics in concert with
in vitro methods can be more effective than
using in vitro methods alone for learning
which of the many disposition pathways
known are relevant for a particular drug.
opportunities if drug developers enter later
phases of clinical development with a better
understanding of the pathways that
influence exposure varia­bility between
individuals or populations, as well as any
potential drug–drug inter­actions that might
occur. The approach we have proposed is
well suited to exploit the wealth of informa‑
tion that has been generated along with
completion of the human genome project.
David A. Katz and Anahita Bhathena are at
Abbott Global Pharmaceutical Research &
Development, 100 Abbott Park Road, Abbott Park,
Illinois 60064-3500, USA.
Bernard Murray is at Gilead Sciences, 333 Lakeside
Drive, Foster City, California 94404, USA.
Leonardo Sahelijo is at Takeda Global Research
& Development, One Takeda Parkway, Deerfield,
Illinois 60015, USA.
Correspondence to D.A.K.
e-mail: [email protected]
doi:10.1038/nrd2486
1.
2.
3.
4.
5.
6.
7.
Conclusions
Genetic variation is only one of several factors
contributing to variability in drug disposi‑
tion. Other factors, such as dose and disease
state, may turn out to be more important
determinants of drug disposition for many
drugs. However, that fact does not lessen the
impetus to discover the situations for which
genetic variation is a major determinant
for the disposition of an NCE, or limit the
potential utility of such information when it
is learned.
Practical limitations to implementation
of our proposed strategy are few, although
changes to study designs in the pharma­
ceutical industry could limit its value
in Phase I drug development. For some
applications, the value may also be limited
because we do not have the ability to act
on the knowledge gained. Nevertheless,
we believe that there are many useful
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Acknowledgements
The authors would like thank B. Spear for his leadership and
mentoring, as well as C. Locke and numerous colleagues in
the Department of Clinical Pharmacokinetics at Abbott for
helpful discussions and collaboration.
Competing interests statement
The authors declare competing financial interests: see web
version for details.
DATABASES
Entrez Gene: http://www.ncbi.nlm.nih.gov/entrez/query.
fcgi?db=gene
ABCB1 | CYP2A6 | CYP2C19 | CYP2C9 | NAT2 | ORM1 |
SLCO1B1 | UGT1A1 | UGT1A9
UniProtKB: http://ca.expasy.org/sprot
BCHE | CYP2C18 | CY2D6 | CYP2R1 | CYP2S1 | CYP3A4 |
FMO1 | FMO2 | FMO3 | OATP1B1 | TPMT
FURTHER INFORMATION
OMIM: http://www.ncbi.nlm.nih.gov/entrez/query.
fcgi?db=OMIM
The Pharmacogenetics and Pharmacogenomics
Knowledge Base: http://www.pharmgkb.org/
SUPPLEMENTARY INFORMATION
See online article: S1 (table) | S2 (table) | S3 (table) |
S4 (table) | S5 (table)
All links are active in the online pdf
volume 7 | april 2008 | 305
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