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Transcript
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nature publishing group
Monoclonal Antibody Pharmacokinetics and
Pharmacodynamics
W Wang1, EQ Wang2 and JP Balthasar3
More than 20 monoclonal antibodies have been approved as therapeutic drugs by the US Food and Drug Administration,
and it is quite likely that the number of approved antibodies will double in the next 7–10 years. Antibody drugs show
several desirable characteristics, including good solubility and stability, long persistence in the body, high selectivity
and specificity, and low risk for bioconversion to toxic metabolites. However, many antibody drugs demonstrate
attributes that complicate drug development, including very poor oral bioavailability, incomplete absorption following
intramuscular or subcutaneous administration, nonlinear distribution, and nonlinear elimination. In addition, antibody
administration often leads to an endogenous antibody response, which may alter the pharmacokinetics and efficacy of
the therapeutic antibody. Antibodies have been developed for a wide range of disease conditions, with effects produced
through a complex array of mechanisms. This article attempts to provide a brief overview of the main determinants of
antibody pharmacokinetics and pharmacodynamics.
Introduction
Antibodies, which are also called immunoglobulins (Igs), are
large proteins used by the immune system to identify and neutralize foreign objects such as bacteria and viruses. All Ig molecules are composed of a basic unit of two identical heavy chains
and two identical light chains, held together by a number of
disulfide bonds. In humans, there are two types of light chains
(κ and λ) and five types of Ig heavy chains (α, δ, ε, γ, and μ).1 Igs
are grouped into five classes according to the structure of their
heavy chains: IgA, IgD, IgE, IgG, and IgM. Among these, IgG
is the predominant class, comprising ~80% of the Igs in human
serum. All of the approved therapeutic antibodies are IgGs, and
this review focuses on this class.
Intact IgGs have a molecular weight of ~150 kDa and a valence
of 2 (meaning that each molecule of IgG contains two identical
antigen-binding domains). The antigen-binding sites are located
in the complementarity determining regions (CDRs) within the
Fab portion of the antibody (Figure 1). Fab, which refers to the
fragment of antigen binding, is composed of domains associated
with the light chain (VL, CL) and domains associated with the
heavy chain (VH, CH1). The stem, or Fc, portion of IgG contains the CH2 and CH3 domains of the heavy chains, and this
region of the antibody is involved with binding to a wide range
of cell-associated receptors (i.e., Fc receptors). The IgG family of
antibodies may be further divided, again based on the structure
of their heavy chains, into four subclasses: IgG1, IgG2, IgG3, and
IgG4. Structural differences among IgG heavy chains lead to differences in subclass binding to Fc receptors and, consequently,
to subclass-specific differences in processes mediated by Fc
receptors (e.g., activation of complement or antibody-dependent
cell-mediated cytotoxicity). For example, antibody-dependent
cell-mediated cytotoxicity by mononuclear cells is more efficient
for IgG1 and IgG3 than for IgG2 and IgG4. On the other hand,
IgG4 is much more active in recruiting the alternative complement pathway than are the other three IgG subclasses.1
Antibody drugs typically possess several desirable pharmacological characteristics, such as long serum half-lives, high
potency, and limited off-target toxicity. Initial antibody therapies were prepared from hyperimmune sera, collected following immunization of animals. The resulting antibody product,
which is derived from a large number of genetically distinct
cells, contains a distribution of Ig isotypes and affinities. In 1975,
Köhler and Milstein demonstrated that antibody-producing
B lymphocytes may be fused with myeloma cells to generate
hybrid cells (hybridomas) that propagate indefinitely in culture
and secrete antibody.2 Cloning the hybridoma cells enabled efficient production of antibody derived from a single progenitor,
and the resulting monoclonal antibody (mAb) preparations are
1Department of Drug Metabolism and Pharmacokinetics, Merck Research Laboratories, West Point, Pennsylvania, USA; 2Department of Pharmacokinetics, Dynamics,
and Drug Metabolism, Pfizer Global Research and Development, Groton Laboratories, Groton, Connecticut, USA; 3Department of Pharmaceutical Sciences, Center for
Protein Therapeutics, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.
Correspondence: JP Balthasar ([email protected])
Received 17 July 2008; accepted 30 July 2008; advance online publication 10 September 2008. doi:10.1038/clpt.2008.170
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homogeneous with respect to antibody isotype, primary amino
acid sequence, affinity, and specificity.
The initial mAbs were generated from mouse and rat hybridomas. These first-generation antibodies found only limited
VH
VL
CL
Fab
CH1
CH2
Fc
CH3
Figure 1 Structure of immunoglobulin gamma (IgG). Four polypeptide
chains, including two identical light chains (~25 kDa) and two identical heavy
chains (~50 kDa), form the structure of IgG. The light chain contains a variable
domain (VL) and a constant domain (CL). The heavy chain is composed of a
variable domain (VH) and three constant domains (CH1, CH2, and CH3). The
regions associated with antigen binding, or Fab sections of the antibody,
include VL, CL, VH, and CH1, whereas the Fc portion of the antibody includes
CH2 and CH3. The constant regions are very similar for all IgG antibodies,
allowing consistency in structure. Differences between antibodies in the
sequence of the variable domains (VL and VH) allow for selective and specific
binding of antibodies to different target epitopes on antigens.
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success in the clinic because of their short half-lives and high
immunogenicity. A number of approaches have been developed to humanize rodent antibodies, from development of
chimeric antibodies (where constant regions are typically
derived from human IgG, and variable regions are derived
from rodent IgG), to “CDR-grafted” antibodies (where CDR
regions are derived from the parental rodent IgG, but the
remaining 90–95% of the antibody is composed of sequence
derived from human IgG), to fully human antibodies. During
the past three decades, significant technology advances have
been achieved in developing and producing mAbs at commercial scale. To date, the US Food and Drug administration
has approved more than 20 mAbs in various therapeutic areas
(Table 1). It is estimated that more than 500 mAbs are currently in development.
Pharmacokinetic and pharmacodynamic (PK/PD) analyses
are essential components of the drug discovery and development process. Antibody drugs often exhibit PK/PD properties
that are much more complex than those typically associated
with small-molecule drugs (i.e., organic compounds with
molecular weight <1,000 Da). Some of the properties had
previously been extensively reviewed.3,4 This report provides
an overview of the primary determinants of antibody pharmacokinetics and pharmacodynamics, with special attention
to state-of-the-art methods of the mathematical modeling of
antibody PK/PD.
Table 1 Monoclonal antibodies marketed for therapeutic use
Antibody
Trade name
Isotype/structure
Primary indication
Abciximab
REOPRO
Chimeric mouse/human Fab
Prevention of cardiac ischemic complications
Adalimumab
HUMIRA
Human IgG1
Rheumatoid arthritis
Alemtuzumab
CAMPATH
CDR-grafted rat/human IgG1
B-cell chronic lymphocytic leukemia
Basiliximab
SIMULECT
Chimeric mouse/human IgG1
Prophylaxis of acute organ rejection
Bevacizumab
AVASTIN
CDR-grafted mouse/human IgG1
Colorectal, lung, and breast cancer
Certolizumab pegol
CIMZIA
PEGylated Fab
Crohn’s disease
Cetuximab
ERBITUX
Chimeric mouse/human IgG1
Head and neck cancer, colorectal cancer
Daclizumab
ZENAPAX
CDR-grafted mouse/human IgG1
Prophylaxis of acute organ rejection
Eculizumab
SOLIRIS
CDR-grafted mouse/human IgG2/IgG4
Paroxysmal nocturnal hemoglobinuria
Efalizumab
RAPTIVA
CDR-grafted mouse/human IgG1
Psoriasis
Gemtuzumab ozogamicin
MYLOTARG
CDR-grafted mouse/human IgG4
Acute myeloid leukemia
Ibritumomab tiuxetan
ZEVALIN
Murine IgG1
Non-Hodgkin’s lymphoma
Infliximab
REMICADE
Chimeric mouse/human IgG1
Rheumatoid arthritis, Crohn’s disease
Muromonab-CD3
ORTHOCLONE OKT3
Murine IgG2a
Acute organ rejection
Natalizumab
TYSABRI
CDR-grafted mouse/human IgG4
Multiple sclerosis
Omalizumab
XOLAIR
CDR-grafted mouse/human IgG1
Asthma
Palivizumab
SYNAGIS
CDR-grafted mouse/human IgG1
Prevention of respiratory tract disease
Panitumumab
VECTIBIX
Human IgG2
Colorectal cancer
Ranibizumab
LUCENTIS
CDR-grafted human IgG1 Fab
Macular degeneration
Rituximab
RITUXAN
Chimeric mouse/human IgG1
Non-Hodgkin’s lymphoma, rheumatoid arthritis
Tositumomab
BEXXAR
Murine IgG2a
Non-Hodgkin’s lymphoma
Trastuzumab
HERCEPTIN
CDR-grafted mouse/human IgG1
Breast cancer
CDR, complementarity determining region; IgG, immunoglobulin G.
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Antibody Absorption
The majority of marketed antibodies are labeled for intravenous
(IV) administration; however, several antibodies have been
approved for extravascular administration. For example, certolizumab pegol, adalimumab, efalizumab, and omalizumab are
all approved for subcutaneous (SC) administration. Palivizumab
is approved for intramuscular (IM) administration, and ranibizumab is administered by intravitreal injection. Antibodies have
not been successfully developed for oral administration, as oral
absorption of antibody is limited by presystemic degradation in
the gastrointestinal tract and by inefficient diffusion or convection through the gastrointestinal epithelium. With the exception
of ranibizumab, where intravitreal administration is employed to
promote a regional effect, efficacy of mAbs following extravascular administration is dependent on systemic absorption.
Primary pathways for systemic absorption include convective transport of antibody through lymphatic vessels and into
the blood, and diffusion of antibody across blood vessels distributed near the site of injection. Based on work conducted
by Supersaxo et al.5 that investigated the lymphatic uptake of a
variety of proteins following SC injection in sheep, it has been
suggested that the majority of antibody administered via SC
or IM injection is absorbed via convection through lymphatic
­vessels. However, recent investigations conducted in rats suggest
that the role of diffusion into blood vessels may be underestimated by the sheep studies.6 Using insulin, bovine serum albumin, and erythropoietin as model proteins, Kagan et al. found
that <3% of the administered dose of each protein was absorbed
via the lymph. Neither Kagan et al. nor Supersaxo et al. have
thoroughly investigated the fate of IgG following SC injection
and, consequently, there is substantial uncertainty regarding the
primary determinants of antibody absorption. The kinetics of
antibody absorption, however, has been well described. After
IM or SC injection, absorption proceeds slowly, and the time
to reach maximal plasma concentrations (tmax) typically ranges
from 2 to 8 days. Absolute bioavailability is generally reported
between 50 and 100%.3
In practical terms, bioavailability is determined by the relative rates of presystemic catabolism and systemic absorption.
Presystemic catabolism may be dependent on rates of extracellular degradation (e.g., via proteolysis), rates of antibody
endocytosis (e.g., receptor-mediated, fluid phase), and rates
of recycling through interaction with the Brambell receptor
(FcRn). FcRn protects IgG from intracellular catabolism, and
FcRn has been shown to be capable of transporting IgG across
cell monolayers in both the apical-to-basolateral and basolateralto-apical directions. Work from the Balthasar Laboratory
(A. Garg, P.J. Lowe, and J.P. Balthasar, unpublished data) has
indicated that the systemic bioavailability of 7E3, a monoclonal
IgG1 antibody, was threefold higher in wild-type mice vs. FcRndeficient mice (82.5 ± 15.6% vs. 28.3 ± 6.9%, P < 0.0001). It
is not yet known whether the effects of FcRn on SC bioavailability are primarily related to FcRn-mediated protection from
catabolism or from FcRn-mediated transport across the vascular endothelium (from interstitial fluid to the blood); however,
the former mechanism is considered to be more plausible.
550
In some cases, an inverse relationship between SC bioavailability and antibody dose has been noted.7 Such relationships
are suggestive of saturable endocytosis and/or saturable degradation processes. Degradation at the injection site is likely to
account for some presystemic loss of antibody, but the quantitative significance is uncertain. Charman et al. have demonstrated
that the major determinant of the SC bioavailability of human
growth hormone in sheep is presystemic catabolism during the
course of lymphatic transport.8 The role of lymphatic catabolism on the bioavailability of other proteins, including mAbs,
is not known.
As a result of limited solubility of antibodies in solution
(~100 mg/ml) and limitations on the volume of fluid that may
be tolerated with IM or SC injection (~5 and 2.5 ml, respectively), IM and SC administration are feasible only for antibodies that demonstrate relatively high dose potency. Use of
multiple injections may help to overcome this limitation, at least
to some extent. For example, doses of 375 mg of omalizumab
are routinely administered clinically, via three separate 1-ml SC
injections.
Although they have not yet been employed in routine clinical
use, there is substantial interest in the development of antibodies
and Fc-fusion proteins for pulmonary delivery.9 The lungs have
a very large surface area and high perfusion rate. In addition,
pulmonary epithelial cells are known to express FcRn, which
may facilitate efficient systemic absorption of antibody delivered
to the lung. As discussed with SC and IM administration, the
feasibility of pulmonary delivery of antibodies is likely limited to
those antibodies associated with very high dose potency, as only
small volumes of fluid may be delivered to the lung.
Antibody Distribution
The distribution of mAbs is determined by the rate of extravasation in tissue, the rate of distribution within tissue, the rate and
extent of antibody binding in tissue, and the rates of elimination
from tissue. For large, polar substances such as mAbs, diffusion
across vascular endothelial cells is very slow, and convection
is believed to be the primary mechanism responsible for the
transport of antibody from blood fluid to interstitial fluids of
tissue. Of note, physiologically based analyses of antibody disposition in mice suggest that >98% of antibody enters tissue via
convection.10 The rate of extravasation by convective transport,
or the movement of antibody into tissue by “solvent drag,” is
determined by the rates of fluid movement from blood to tissue
and by the sieving effect of paracellular pores in the vascular
endothelium. Sieving is thought to be largely determined by
the size and tortuosity of the pores and by the size, shape, and
charge of the solute (i.e., the antibody). Most physiologically
based models of antibody disposition describe the uptake clearance for antibody extravasation as a product of the lymph flow
rate (L) and an efficiency term (1 − σ). The reflection coefficient,
σ, represents the fraction of solute sieved during the movement
of solvent through a pore. In the case of mAbs, tissue reflection
coefficients are often assumed to be equal in all tissues, with
values in the range of 0.95–0.98.10–12 However, it is likely that
reflection coefficients may be much lower in tissues such as the
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spleen, liver, and bone marrow, where the vascular endothelium
is known to be fenestrated or “leaky.”
The rate of antibody elimination from tissue will be primarily determined by the convective elimination clearance and by
the rates of antibody catabolism within tissue. As with uptake
clearance, convective elimination clearance will be a function
of the fluid flow rate (i.e., the rate of lymph flow) and sieving.
The reflection coefficient associated with convective elimination clearance is related to the diameter of the lymphatic vessels,
which carry fluid that drains from the interstitial spaces of tissue.
Lymphatic vessels are much larger than paracellular pores in
the vascular endothelium and, consequently, it is assumed that
there is relatively little restriction of the convective movement
of antibody through the lymph. As such, physiologically based
models typically assume reflection coefficients of 0–0.2 for convective elimination of antibody via lymphatic drainage. Because
of the differences in the efficiency of convective uptake into tissue and convective elimination of antibody from tissue, antibody
concentrations in tissue interstitial fluid are substantially lower
than antibody concentrations in plasma. In many tissues, concentrations of unbound IgG are approximately tenfold lower
than concentrations in plasma; however, higher concentrations
are observed in tissues with leaky vasculature (e.g., bone marrow and spleen).
IgG antibodies show very little distribution to the brain. The
ratio of IgG concentration in the brain relative to plasma is
reported to be in the range of 1:500. The poor distribution of antibody to the brain may be explained, in part, by inefficient convective uptake into the brain (e.g., because of the “tight junctions” in
the brain vascular endothelium) and by rapid turnover of brain
interstitial fluids, which would correlate with efficient convective
elimination of IgG from the brain. There is some evidence to suggest that specific Fc receptors, perhaps including FcRn, actively
efflux IgG from brain tissue.13 However, investigations conducted
in the Balthasar Laboratory have demonstrated virtually identical
IgG brain-to-plasma exposure ratios following an IV dose of a
murine monoclonal IgG1 antibody in wild-type animals and in
FcRn-deficient mice (i.e., 0.0022 ± 0.00015 vs. 0.0021 ± 0.00011,
P = 0.3347, A. Garg and J.P. Balthasar, unpublished data). Further
investigation is needed to evaluate the possible significance of Fc
receptor–mediated efflux of IgG from the brain.
Following extravasation, antibody distribution within the
interstitial space of tissues is driven by diffusion and convection and, in some cases, restricted as a result of processes of
tissue catabolism and cell binding. Some high-affinity mAbs
have been reported to show very limited distribution within
tissue, where antibody appears to be confined to regions surrounding blood vessels. Nonhomogeneous distribution of antibodies in such tissues has been explained by the “binding-site
barrier” hypothesis, which proposes that antibody distribution
is restricted because of tight binding to cells near the sites of
antibody extravasation.14 The binding-site barrier may be overcome, in theory, with the use of large doses of antibody that
may saturate binding sites; however, it may be impossible to
administer sufficient doses because of off-target toxicity and/
or feasibility issues.
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Analysis of antibody distribution is much more complicated
than the analysis of the distribution of most small-molecule
drugs. Small-molecule drugs are typically eliminated by the kidney and liver. As a result of the high rate of perfusion of these
organs, and the high tissue permeability of most small-molecule
drugs, concentrations of drug at the site of drug elimination (i.e.,
liver and/or kidney) appear to be in very rapid equilibrium with
the concentration of drug in plasma. For such drugs, the apparent volume of distribution at steady state (Vss) is independent of
the rate of elimination clearance, and Vss may be inferred from
plasma data through the use of standard noncompartmental
analyses or through data-fitting with mammillary compartmental models. For macromolecular protein drugs, it is possible,
and perhaps likely, that a significant fraction of drug elimination occurs from tissue sites that are not in rapid equilibrium
with plasma. In such situations, noncompartmental analysis of
plasma data will lead to an underestimation of Vss.3 In cases
where antibody shows high-affinity, high-capacity binding in
tissue, and “target-mediated elimination,” the true Vss may be
more than tenfold greater than the distribution volume estimated by standard noncompartmental analyses. When significant drug elimination occurs from “peripheral compartments,” it
is not possible to obtain precise estimates of Vss from analysis of
plasma data alone; however, Mordenti and Rescigno have shown
that, in certain situations, plasma data may be used to define the
range of possible values for the distribution volume.15 Precise
analysis of Vss requires concentration data from both plasma
and tissues.
Antibody Elimination
Common mechanisms of drug elimination include filtration
(e.g., into urine), secretion (e.g., into the bile), and biotransformation (e.g., metabolism or catabolism). Renal elimination,
which is a primary pathway of clearance of small-molecule
drugs, is relatively unimportant for IgG, as its large size prevents
efficient filtration through the glomerulus. Secretion into the
bile is an important pathway of elimination of IgA antibodies,
but this route is not a significant contributor to the elimination of IgG antibodies. The majority of IgG elimination occurs
via intracellular catabolism, following fluid-phase or receptormediated endocytosis.
Receptor-meditated endocytosis of IgG may proceed following
interaction of the Fab binding domains of the antibody with target epitopes found on the cell surfaces. This type of endocytosis
and elimination is a form of target-mediated disposition where
the interaction of the drug and its pharmacological target (e.g., a
target receptor) serves as a significant contributor to the kinetics
of antibody distribution and elimination. Target-meditated elimination is, by definition, capacity limited (saturable) because of
finite expression of the target. The rate of uptake and elimination
of antibodies by target-mediated pathways is a function of dose
and the expression level of the target, as well as a function of the
kinetics of receptor internalization and intracellular catabolism.
It is important to note, however, that target-mediated elimination does not necessarily require Fab binding to a cell-surface
receptor. Certain soluble substances, particularly multimeric
Clinical pharmacology & Therapeutics | VOLUME 84 NUMBER 5 | NOVEMBER 2008
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substances with several repeated epitopes, may bind with two
or more antibodies, leading to the formation of large complexes
that may be rapidly eliminated by phagocytosis. Elimination of
large immune complexes may explain, in part, the nonlinear
elimination kinetics of omalizumab and denosumab, which are
thought to interact with soluble targets (IgE and receptor activator of nuclear factor-κB ligand). The majority of marketed
antibodies demonstrate dose-dependent elimination consistent
with target-mediated elimination, where clearance decreases as
a function of dose (e.g., trastuzumab, rituximab, gemtuzumab,
and panitumumab). Examples of state-of-the-art mathematical modeling of target-mediated antibody elimination include
reports by Ng et al., describing the nonlinear disposition of
TRX1, an anti-CD4 mAb;16 by Hayashi et al., describing the
nonlinear disposition of omalizumab, an anti-IgE mAb;17 and
by Lammerts van Bueren et al., presenting an interesting conceptual model of target-mediated antibody elimination from a
peripheral distribution compartment.18
IgG antibodies may also interact with Fc-γ-receptors (FcγR),
and IgG-FcγR complexes may trigger endocytosis and catabolism. Considering the relatively high affinity of IgG for FcγR
and the high endogenous concentrations of IgG in plasma
(~65 μmol/l), it has been argued that FcγR-mediated elimination
is unlikely to be important for monomeric IgG.3 It is possible
that FcγR-mediated elimination is significant, and perhaps dominant, in cases where antibody is able to form soluble immune
complexes containing three or more IgG molecules, as well as
in cases where antibody binds to cells suspended in blood or
other body fluids (perhaps including viruses, bacteria, platelets, erythrocytes, and leukocytes). IgG “opsonized” particles are
rapidly engulfed following engagement of FcγR on macrophages
and on other phagocytic cells. This mechanism of elimination
is well supported by the immunology literature; however, little
work has been performed to link FcγR-mediated phagocytosis
to the systemic pharmacokinetics of therapeutic antibodies.
Additional study is required to allow meaningful discussion of
the role of FcγR-mediated endocytosis in the elimination of such
antibodies.
IgG, like other proteins found in plasma and interstitial
fluid, may enter cells in all tissues via fluid-phase endocytosis.
Interestingly, however, IgG differs from most proteins in that
a significant fraction of endocytosed IgG is not sorted to the
lysosome but is redirected to the cell surface and released into
plasma or interstitial fluids. The recycling of IgG is mediated
by the Brambell receptor, FcRn, which binds to IgG with pHdependent affinity.19,20 Within the acidified environment of the
early endosome, IgG binds tightly to FcRn. The IgG–FcRn complexes are not delivered to the lysosome for catabolism but rather
are sorted to the cell surface for fusion with the cell membrane.
The receptor shows virtually no affinity for IgG at physiological
pH and, upon fusion of the sorting vesicle with the cell membrane, IgG dissociates from the receptor and is rapidly released
into extracellular fluid.
FcRn-mediated recycling of IgG appears to be quite efficient based on studies conducted with knockout mice. In animals lacking expression of FcRn, IgG clearance is increased
552
by approximately tenfold,20 which would be consistent with a
recycling efficiency of 90% (i.e., in wild-type animals expressing FcRn). Because FcRn expression is limited, FcRn-mediated
recycling is capacity limited. The average concentration of IgG
in plasma in humans is ~10 mg/ml. At this concentration, IgG
has a half-life of ~25 days21 and a plasma clearance of ~10 ml/h
(~3.5 ml/kg/day). High concentrations of IgG are able to saturate
the recycling system, decreasing recycling efficiency and leading
to an increase in the fractional catabolic rate of IgG. For example, in myeloma patients, where IgG concentrations in plasma
may approach 100 mg/ml, IgG half-life decreases to 8–10 days.
Conversely, in patients with very low plasma concentrations of
IgG, the half-life of IgG antibody may be >70 days.21
IgG affinity for FcRn is species specific. Human FcRn shows
high affinity for human IgG and also for IgG from guinea pigs
and rabbits; however, the human receptor shows very little affinity for IgG derived from most other species, including mice and
rats.22 The low affinity of human FcRn for mouse IgG helps to
explain the very rapid elimination of murine mAbs in humans.
Approved murine monoclonal IgGs (e.g., muromononab-CD3,
ibritumomab) demonstrate half-lives of ~1 day in patients,
whereas human IgG is typically associated with a half-life of
~25 days.
Although FcRn recycling is capacity limited, significant alteration in the efficiency of FcRn recycling is not typically achieved
with therapeutic doses of mAbs. Most mAbs are administered at
doses of <10 mg/kg, which will increase the total IgG “body load”
by <1–2%, as humans typically possess 50–100 g of endogenous
IgG. However, high-dose intravenous immunoglobulin (IVIG)
therapy, which calls for the administration of 2 g/kg of pooled
human IgG, increases IgG plasma concentrations sufficiently to
increase IgG clearance approximately threefold.23 This increase
in IgG clearance leads to a decrease in endogenous antibody
concentrations; consequently, IVIG therapy for the treatment of
autoimmune conditions may achieve effects by decreasing the
plasma concentrations of endogenous, pathogenic autoantibodies. Although IVIG therapy is an effective treatment of a variety
of autoimmune conditions, it is very expensive because of the
high doses of antibody required. Of note, preclinical experiments
have demonstrated that anti-FcRn antibodies are able to achieve
effects similar to those of IVIG therapy, at dose levels that are
~100-fold lower than those required for use in IVIG therapy.24
There is significant interest in the development of specific FcRn
inhibitors for use in the treatment of autoimmunity.25
Immunogenicity
Any exogenous protein may be viewed by the body as foreign
and trigger immune responses that lead to the generation of
endogenous antibodies against the protein. Therapeutic antibodies are no exception. mAb drugs may be categorized as
(i) rodent antibodies, which are typically obtained from murine
or rat hydridomas; (ii) chimeric antibodies, which are derived
from chimeras that have been engineered to express IgG antibodies with human constant regions and rodent variable regions; (iii)
CDR-grafted antibodies, which contain specific regions within
rodent variable domains, the CDRs, grafted onto a human IgG
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framework; and (iv) antibodies that are fully human. In chimeric
antibodies, ~67% of the primary sequence of the antibody is
derived from the human sequence, and ~33% is derived from the
rodent sequence. In the case of CDR-grafted antibodies, ~95% of
the primary sequence is derived from a human IgG, and ~5% is
derived from a rodent antibody. Immunogenicity (i.e., the ability of a protein to induce immune response) is associated with
the fraction of foreign sequence in the therapeutic antibody.26
For example, when immunogenicity of a CDR-grafted form of
anti-Tac antibody and its murine counterpart were compared in
cynomolgus monkeys, it was found that monkeys treated with
the humanized version exhibited five- to tenfold lower anti-drug
antibody titers.27 Early rodent mAbs were shown to be highly
immunogenic in humans. Muromonab-CD3 (OKT3), the first
murine mAb approved by the Food and Drug administration,
has been associated with high incidences of anti-drug antibodies
that interfere with its function.28 In contrast, chimeric, humanized, and fully human therapeutic antibodies are associated with
much lower frequency of immunogenicity, ranging from <1% to
~10% in most cases. Of note, however, assays for immunogenicity are difficult to develop and verify, and the direct comparison
of immunogenicity results is complicated by potential differences in assay sensitivity.28 Nevertheless, it is fair to say that all
therapeutic antibodies currently on the market have shown at
least some immunogenicity. Even fully human antibodies have
unique idiotypes and, in some cases, unique posttranslational
modifications or impurities associated with the manufacturing
process that may trigger an immune response.
Other factors associated with immunogenicity include duration of therapy, dose, and route of administration.26 Perhaps as
expected, the degree of immunogenicity increases with duration of therapy. In the case of infliximab, a chimeric antitumor
necrosis factor antibody, anti-drug antibodies are infrequently
detected within the first 2 months of therapy; however, after
12 months of therapy, anti-drug antibodies are found in >90%
of treated patients.29 Interestingly, anti-drug antibodies are
found more frequently, in many cases, after low-dose therapy
vs. after higher doses of therapeutic antibody. For example,
Stephens et al. demonstrated that after administration of the
antibody CDP571 to human subjects at doses ranging from
0.1 to 5 mg/kg, anti-CDP571 titers decreased with increasing
dose.30 This phenomenon, which has been reported for other
therapeutic antibodies, may reflect an actual inverse relationship
between immunogenicity and dose; however, the data may also
be explained by assay interference (i.e., where the presence of
higher quantities of drug in the sample affects the ability of the
assay to detect anti-drug antibodies). Several reports suggest
that administration of biologics by the SC and IM routes leads
to greater immunogenicity than that observed following the IV
route; however, this has not been convincingly demonstrated
in humans.
Immunogenicity can affect the safety, pharmacokinetics, and
pharmacodynamics of therapeutic antibodies. The clinical significance of immunogenicity is product specific and has been
reviewed extensively elsewhere.31 The presence of anti-drug
antibodies may lead to a wide range of PK effects, and the effect
art
of endogenous, anti-drug antibodies on a given exogenous protein may depend on the number of antigenic sites found on the
exogenous protein. In cases where only one or two endogenous
anti-drug IgG molecules bind to the exogenous protein, the halflife of the exogenous protein may actually increase and approach
that of endogenous IgG. On the other hand, in cases where three
or more IgGs bind to the exogenous protein simultaneously,
the resulting immune complex will be eliminated very rapidly
through phagocytosis.32 The impact of endogenous anti-drug
antibodies on the disposition of therapeutic antibodies may be
complex and difficult to predict. However, it is reasonable to
expect that, with increased exposure to a therapeutic antibody,
there is an increasing probability of development of endogenous
antibodies against multiple antigenic sites, as well as an increasing probability that endogenous antibodies will mediate rapid
elimination of the therapeutic antibody.
Interspecies Scaling
In many cases, PK parameters of small-molecule drugs may be
scaled across species, via the principles of allometry, with reasonable precision. Such scaling is often accomplished using a simple
power model of the form Y = a BWb, where Y is the parameter
of interest, BW is the body weight, a is the allometric coefficient,
and b is the allometric exponent. Allometric scaling was first
applied to proteins by Mordenti et al. in 1991.33 In their work,
the authors applied power models to five therapeutic proteins,
including a fusion protein composed of CD4 and the Fc portion
of an IgG1 molecule. It was shown that the clearance and volume
data from preclinical species can be satisfactorily described by
the power equation, and the predictions of clinical parameters
were very close to the observed values. Interestingly, exponent
values for clearance and volume were close to the “expected”
values of 0.75 and 1 for small-molecule drugs. Other examples of
successful application of allometric scaling to proteins and antibody drugs include work by Grene-Lerouge et al. for antibody
fragments,34 Woo and Jusko (2007) for human recombinant
proteins (erythropoietin),35 and Vugmeyster et al. for mAbs.36
Although these reports suggest that allometric power relationships may be used to predict clinical antibody pharmacokinetics,
one must proceed with caution. Assumptions underlying allometric scaling include the absence of nonlinear pharmacokinetics
and species-specific clearance, which may not hold true for most
therapeutic antibodies. For example, in an unsuccessful attempt,
allometric scaling failed to predict clearance of a murine antiEGF/r3 mAb in cancer patients.37 This is likely because of both
the low affinity of murine antibodies for human FcRn and the
increased target-mediated clearance in cancer patients.
Another approach for predicting drug pharmacokinetics in
humans based on preclinical data is to employ physiologically
based PK (PBPK) modeling. PBPK models represent the body
with several interconnected compartments, each representing an
organ. The size of each compartment is based on the physical size
of distribution spaces within the organ of interest, and the intercompartmental transfer functions relate to physiological processes (e.g., blood perfusion and lymph flow). Data obtained in
preclinical species, together with actual physiological parameters
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art
such as volume of each compartment (organ) and blood flow
rate, are used to build the model and predict human pharmacokinetics. A significant advantage of the PBPK approach is that
it allows prediction of antibody levels in many tissues, including
tumor. PBPK models are ideally suited to the consideration of
effects of saturable processes (e.g., target binding, FcRn processing) on antibody pharmacokinetics, and these models are also
well suited to predict the influence of a variety of factors (e.g.,
antigen expression, antibody affinity) on the tissue selectivity of
antibody disposition. Recent PBPK models have incorporated
FcRn-antibody binding, allowing consideration of the effects
of FcRn on antibody catabolism and distribution.11,12 The limitations for use of PBPK models to predict the disposition of
antibodies in humans are significant, however. PBPK models
are complex, mathematically difficult to construct, poorly suited
to population analyses, and often limited because of a lack of
tissue concentration data, parameter availability, or parameter
identifiability.
Despite the large number of antibodies in development, only
a handful of reports using preclinical data to predict the clinical
pharmacokinetics of antibodies have been published, perhaps
indicating the difficulties associated with the interspecies scaling
of antibody disposition. Any effort to predict human pharmacokinetics based on preclinical disposition data should consider possible species differences in the expression or turnover of the target
receptor, antibody affinity for the target, antibody-FcRn binding,
endogenous IgG concentrations (i.e., as a determinant of FcRn
saturation), and potential effects of host anti-drug antibodies.
Pharmacodynamics
mAbs have been marketed for use in the treatment of a wide
range of conditions, including cancer, autoimmunity, and
inflammatory diseases. It is convenient to discuss antibody
pharmacodynamics relating to four main categories of applications: (i) immunotoxicotherapy, where antibody is employed to
alter the pharmacokinetics and pharmacodynamics of soluble
ligands (e.g., drugs, xenobiotics, and cytokines); (ii) elimination
of target cells; (iii) alteration of cellular function (e.g., receptor
blockade); and (iv) targeted drug delivery.3
Antibodies used for immunotoxicotherapy include bevacizumab, adalimumab, ranibizumab, omalizumab, and infliximab.
Each of these antibodies binds to a soluble ligand (e.g., vascular
endothelial growth factor or tumor necrosis factor) and alters
the pharmacokinetics and pharmacodynamics of the ligand.
These “neutralizing” antibodies act as competitive inhibitors of
ligand-receptor binding, shifting ligand concentration–effect
relationships. In addition, by binding to soluble ligand, immunotoxicotherapies often produce dramatic alterations in ligand
pharmacokinetics. In most cases, the anti-ligand antibody will
decrease the unbound fraction of ligand in plasma, decrease the
ligand volume of distribution and clearance, and increase the halflife of the ligand. For example, omalizumab, an anti-IgE mAb,
dramatically decreases the clearance of its target ligand, leading
to a fivefold increase in the plasma half-life of IgE.
PK/PD models for omalizumab and infliximab have been published recently.38,39 In each model, a second-order association
554
function was employed to describe the formation of antibody–
ligand complexes, and complexes dissociated via a first-order
process. The models are nonlinear with respect to antibody–­
ligand binding because of the second-order nature of the binding
process, as well as capacity limitations associated with the available concentrations of ligand and antibody. The models relate
unbound ligand concentration to the effect of interest; as such,
the models link the PK effects of the anti-ligand antibody to the
pharmacodynamics of the ligand. Of note, the models differ substantially in terms of their characterization of the fate of the antibody–ligand complex. In the infliximab model, it was assumed
that the antibody–ligand complex was eliminated with the same
fractional catabolic rate as the unbound ligand, tumor necrosis
factor-α. Fitting the model parameters to the data resulted in an
estimation of a 30–40-day half-life for tumor necrosis factor-α,
which is considerably different from the known value (<1 h).
The omalizumab model, which is much more plausible, does not
assume an equivalent elimination rate constant for the complex
and the ligand (IgE) but allows for kinetically distinct elimination
of IgE, omalizumab, and the IgE–omalizumab complex.
The recent work of Marathe et al., which describes the PK/
PD of denosumab, a monoclonal IgG2 antibody directed
against the receptor activator of nuclear factor-κB ligand, represents the state of the art in modeling immunotoxicotherapies (Figure 2).40 The receptor activator of nuclear factor-κB
ligand is thought to be a soluble ligand, but there is some possibility that the protein is also expressed on the cell surfaces.
Denosumab pharmacokinetics were captured with a target­mediated disposition model, and denosumab pharmacodynamics were described with a model that relates the unbound
concentrations of denosumab to the inhibitory effect of the
antibody on the receptor activator of nuclear factor-κB ligand
binding. This mechanistic model provided an excellent description of the pharmacokinetics and pharmacodynamics of denosumab in multiple myeloma patients.
Several antibodies, including rituximab, cetuximab, and trastuzumab, are designed to bind to cell-surface proteins to mediate
Osteoclast
precursors
Denosumab–RANKL
Active
osteoblasts
RANKL
RANK–RANKL
Active
osteoclasts
OPG–RANKL
Serum NXT
Figure 2 Pharmacodynamic model for denosumab. Marathe et al. provide
an excellent example of a pharmacodynamic model for an antibody acting
as an antagonist of a soluble ligand.40 Denosumab, like other antibodies
used for immunotoxicotherapy, binds to a soluble ligand (receptor activator
of nuclear factor-κB ligand, RANKL), preventing the ligand from binding to
its endogenous receptor (receptor activator of nuclear factor-κB, RANK),
and antagonizing the effect of the ligand (i.e., inhibiting RANKL stimulation
of osteoclast maturation). The Marathe et al. model, which has been
simplified herein, employs equilibrium binding functions to relate plasma
concentrations of denosumab, RANK, and the natural RANKL antagonist
(osteoprotegrin, OPG) to unbound concentrations of RANKL, and to the
measured biomarker (serum N-telopeptide, NTX).
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state
the destruction of target cells. Antibodies may eliminate cells by
blocking or cross-linking cellular receptors and inducing apoptosis or by “effector functions” of the immune system (e.g., the
classical complement pathway, FcγR-mediated phagocytosis).
The efficacy of an IgG antibody in mediating cellular destruction
is determined by several factors, including the antibody isotype,
the expression of the target protein on the cell surface, the signaling pathways associated with the target protein, and the availability of complement proteins (e.g., C1q) and effector cells (e.g.,
macrophages). Variation in target expression or in the expression
of receptors associated with effector pathways may be expected
to lead to interindividual variability in antibody pharmacodynamics. Indeed, the efficacy of anti-CD20 antibodies (e.g., rituximab) is significantly different in patients expressing FcγRIIIa with
valine vs. phenylalanine at position 158 in the primary sequence
of the receptor.41 Although a wealth of information is available
in the immunology literature that describes the relationships
between effector components and antibody-dependent cell
cytotoxicity, little work has been undertaken to incorporate this
information within mechanistic PK/PD models.
There has been little modeling of the pharmacodynamics of
marketed antibodies that mediate cell destruction. Meijer et al.
have presented a mechanistic model describing the target-mediated disposition of an anti-CD3 antibody, including characterization of the time course of CD3 in the plasma of treated
patients,42 and Mould et al. have presented a semi-mechanistic
PK/PD model describing the effects of anti-CD4 mAb administration on CD4-positive T cells in patients with rheumatoid
arthritis.43 PD models have not been published, to the authors’
knowledge, for rituximab, trastuzumab, or cetuximab.
However, several reports have described the PK/PD modeling
of preclinical data. For example, Sharma et al. developed an indirect effect model to describe the pharmacodynamics of keliximab
and clenoliximab, which are monkey/human chimeric antibodies
directed against CD4.44 Although keliximab and clenoliximab
have identical variable domains, keliximab is an IgG1 and clenoliximab is an IgG4. Relative to IgG4 antibodies, IgG1 antibodies
demonstrate greater potential for antibody-dependent cell cytotoxicity and activation of complement by the classical pathway.
Consequently, keliximab would be expected to be more efficient
in eliminating CD4-positive T cells. Consistent with this expectation, the PD modeling demonstrated that keliximab enhanced
the elimination of CD4-positive T cells with much greater
potency than did clenoliximab. The fit value for SC50, which
refers to the antibody concentration leading to half-maximal
effect, was tenfold lower for keliximab vs. clenoliximab. Similar
models have been developed to describe the pharmacodynamics
of anti-­platelet antibodies in mouse and rat models of immune
thrombocytopenia.45 The Deng model is shown in Figure 3 as an
example of characterization of the pharmacodynamics of antibodies used to stimulate the elimination of target cells.
Abciximab, basiliximab, daclizumab, and efalizumab are examples of marketed antibodies that achieve effects by altering cell
signaling. In addition, some of the effects induced by rituximab
and cetuximab are associated with their effects on signaling
pathways. Abciximab is a Fab fragment that binds to the GPIIb/
I3
τ
I2
kin
τ
PLT
I1
τ
IPLT x
art
PLT −R0
R0
kout
APAb
IVIG
CL
Figure 3 Pharmacodynamic model for anti-platelet antibodies. The Deng
et al. model, selected as an example model for antibodies that mediate the
elimination of cells, utilizes an indirect response function to describe the
stimulatory effect of anti-platelet antibodies on the elimination of platelets.
Platelet production proceeds by a cascade of events (e.g., megakaryoctye
maturation), and this is captured through the use of a transduction
function, where τ represents the time delay associated with each step in the
maturation process. Platelet count provides feedback inhibition on platelet
production (I3, kin). Intravenous immunoglobulin (IVIG) treatment leads to
a stimulation of anti-platelet antibody clearance (CL) via saturation of FcRn
and also leads to an inhibition in the elimination (kout) of opsonized platelets.
Figure adapted from ref. 45.
IIIa receptor on platelets. By binding to the receptor, abciximab
competitively inhibits platelet binding to fibrinogen and the
von Willebrand factor, thereby inhibiting platelet aggregation.
Abciximab, as a Fab fragment, does not possess the Fc-domains
required for mediating antibody-dependent cell cytotoxicity, and,
consequently, abciximab does not typically induce thrombocytopenia in patients. Intact anti-GPIIb/IIIa antibodies, on the other
hand, lead to significant thrombocytopenia in animal models.
Mager et al. have developed an inhibitory Emax model to describe
the effects of abciximab on ex vivo platelet aggregation, using
data obtained from patients undergoing coronary angioplasty.46
The PD model, which assumed that abciximab concentrations in
plasma are directly related to abciximab effects, provides excellent characterization of the data and serves as a good example for
use in the analysis of “direct effects” of antibody drugs.
Efalizumab binds to CD11a, which is a subunit of leukocyte
function antigen-1, and triggers a decrease in the cellular expression of the receptor. By decreasing receptor expression and by
binding to and occupying the available CD11a, efalizumab is an
effective inhibitor of CD11a-mediated cell signaling. The effects
of efalizumab on CD11a expression and on CD3-positive lymphocytes in chimpanzees and in psoriasis patients have been
captured with mechanistic PK/PD models.47
Two models were developed for analysis of data collected
from chimpanzees, each of which utilized an indirect response
relationship to describe CD11a turnover. Although the models differed in terms of their characterization of the pathways
associated with the nonlinear elimination of the antibody, each
model provided very good characterization of the PK/PD data.
Subsequent dose-ranging studies were conducted in psoriasis
patients, and a population PK/PD approach was used to characterize the data using each model. Again, each model was able
to capture the data well. More recently, Ng et al. reported a
mechanistic PK/PD model in which the authors characterized
the kinetic relationships between plasma efalizumab exposure
and efalizumab effects on CD11a expression on T cells and on
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state
art
Vm · efalizumab·CD11a
Vc · Km + efalizumab
CD11a
CD11a
production
kel
kPASI
PASI
kheal
Figure 4 Pharmacodynamic model for efalizumab. The Ng et al. model,
selected as an example model for antibodies that alter cellular function,
describes the effects of efalizumab on CD11a expression, elimination, and
the psoriasis area and severity index (PASI). The model allowed excellent
characterization of efalizumab pharmacodynamics in a large, population
pharmacokinetic and pharmacodynamic analysis. The figure shown is a
simplified version of the model presented by Ng et al.48
the psoriasis area and severity index, an efficacy end point for
psoriasis (Figure 4).48
Preclinical modeling examples include a report by Luo et al.
that describes the development of a model of cetuximab PK/
PD using data collected from studies conducted with a murine
colon carcinoma xenograft model.49 Although the antibody
demonstrates nonlinear, target-mediated disposition in humans,
cetuximab pharmacokinetics were dose-proportional in the
mouse model and were well characterized with a linear, one­compartment model. Cetuximab’s effects on the phosphorylation of the epidermal growth factor receptor were captured with
an indirect effect model, which allowed comparison between
estimated values of EC50 and EC90 (half maximal effective
concentration and 90% effective concentration, respectively),
plasma concentrations of cetuximab achieved in patients, and
the efficacy of cetuximab in clinical trials.
In comparison with the other main categories of antibody
usage, relatively little success has come from the development
of antibodies for targeted drug delivery. Most of the interest
in this area has centered on the development of conjugates
of antibodies and toxic agents (e.g., chemotherapeutic drugs,
radioisotopes, and biological toxins), with the intent of using
the high specificity and selectivity of antibodies to mediate
targeted delivery of toxins. The antibody–toxin conjugates, or
immunotoxins, carry the complexities shared by other types of
antibody drugs (e.g., potential for nonlinear target-mediated
disposition, immunogenicity). In addition, off-target toxicity is often a greater concern for immunotoxins because of
the potential for dissociation, in vivo, of the toxin from the
antibody and because of the high potency of toxins employed.
Considerable toxicity often results from “nonspecific” distribution of the immunotoxin to off-­target sites. Bone marrow stem
cells are particularly susceptible to toxicity from immunotoxins because of their rapid growth rate and high sensitivity to
chemotherapy, along with the leaky vasculature of the bone
marrow, which allows relatively efficient convective uptake of
immunotoxins.
556
Most of the work associated with the use of antibodies for
targeted drug delivery has been focused on the treatment of
solid tumors. Solid tumors are problematic targets for antibody drugs, as tumor growth often leads to the collapse of lymphatic vessels within the tumor, which leads to an increase in
the tumor interstitial pressure. High interstitial pressure minimizes the blood-to-tumor hydrostatic pressure gradient, and
this decreases the driving force for antibody uptake into tumor
by convection. Once antibody extravasates, distribution may be
limited by the binding-site barrier (discussed above), further
reducing the effectiveness of antibody-directed delivery of toxins
to solid tumors.
For chronic immunotoxin therapy, it may be important to
select a cellular target that is easily accessed by antibody in blood
(i.e., hematological cells, cells in tissues with “leaky” vasculature), antibodies with little risk for immunogenicity, toxins with
little risk for immunogenicity (e.g., protein toxins such as ricin
would not be desired), and conjugation chemistry that allows for
little off-target release of toxin, but where there is efficient release
of toxin within target cells (i.e., in cases where this is required for
efficacy). Successfully marketed antibodies include gemtuzumab
ozogamicin, tositumomab, and ibritumomab tiuxetan. In each
case, the antibodies target hematological cells. Tositumomab
and ibritumomab utilize radioisotope toxins, where dissociation
from the antibody is not required for the desired cytotoxic effect.
Gemtuzumab ozogamicin employs a calicheamicin derivative
toxin that is released in target cells after binding of gemtuzumab to the target receptor (CD33) and after receptor-mediated
endocytosis of the immunotoxin. The toxin migrates to the
nucleus and binds DNA, leading to double-strand breaks and
cell death.
There are few publications of PK/PD models for immunotoxin therapies. Ideally, mathematical models of immunotoxin
pharmacokinetics and pharmacodynamics should account for
the intact immunotoxin, “naked” antibody (i.e., antibody alone,
following release of the toxin), and “free” toxin. In an interesting example, Zhu et al. applied physiologically based modeling
and simulation to investigate relationships between the dose of
radioimmunotoxins and uptake of the conjugates into tissue.50
Their modeling led to the conclusion that Fab fragments would
be preferred for use in detection of tumors, whereas Fab2 fragments were predicted to be more effective for use in radioimmunotherapy.50 The structure of their PBPK model may be easily
adapted to the prediction and characterization of the PK/PD of
additional immunotoxins.
Conclusions
Antibody drugs demonstrate unique, complex PK characteristics.
Absorption following IM or SC administration is slow and, for
some antibodies, dose dependent. Antibody distribution kinetics
is influenced by rates of convective transport, binding to tissue
sites, and rates of catabolism within tissue. Traditional noncompartmental analyses and mammillary models may underestimate
the steady-state distribution volume of many antibodies, particularly those associated with substantial elimination from tissue
sites. Antibodies often demonstrate target-meditated disposition,
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where antibody–antigen binding influences the rate and extent of
antibody distribution and elimination. FcRn, the Brambell receptor, protects IgG antibodies from elimination, but this protection
system is saturable. Because of possible capacity limitations in presystemic catabolism, antibody binding to the target antigen, and
FcRn-mediated transport, most antibodies demonstrate nonlinear, dose-dependent pharmacokinetics. Antibodies may be used
for a variety of therapeutic applications, and several mechanisms
may be associated with antibody effects. State-of-the-art mathematical models have, in many cases, allowed successful characterization of the plasma pharmacokinetics of antibody drugs
and the time course of antibody effects. This research field is in its
infancy, however, and there is great need to ­incorporate findings
from basic research within mechanistic PK/PD models to facilitate efforts to predict antibody safety and efficacy in humans.
Acknowledgments
This work was supported by the University at Buffalo/Pfizer Strategic
Alliance and by the National Institutes of Health grants HL67347, AI60687,
and CA118213.
Conflict of Interest
The authors declared no conflict of interest.
© 2008 American Society for Clinical Pharmacology and Therapeutics
1. Frazer, J.K. & Capra, J.D. Immunoglobulins: structure and function. In
Fundamental Immunology 4th edn. (ed. Paul, W.E.) 37–74 (Lippincott-Raven,
Philadelphia, PA, 1999).
2. Köhler, G. & Milstein, C. Continuous cultures of fused cells secreting antibody
of predefined specificity. Nature 256, 495–497 (1975).
3. Lobo, E.D., Hansen, R.J. & Balthasar, J.P. Antibody pharmacokinetics and
pharmacodynamics. J. Pharm. Sci. 93, 2645–2668 (2004).
4. Mould, D.R. & Sweeney, K.R. The pharmacokinetics and pharmacodynamics
of monoclonal antibodies—mechanistic modeling applied to drug
development. Curr. Opin. Drug Discov. Devel. 10, 84–96 (2007).
5. Supersaxo, A., Hein, W.R. & Steffen, H. Effect of molecular weight on the
lymphatic absorption of water-soluble compounds following subcutaneous
administration. Pharm. Res. 7, 167–169 (1990).
6. Kagan, L., Gershkovich, P., Mendelman, A., Amsili, S., Ezov, N. & Hoffman, A. The
role of the lymphatic system in subcutaneous absorption of macromolecules
in the rat model. Eur. J. Pharm. Biopharm. 67, 759–765 (2007).
7. Mortensen, D.L. et al. Pharmacokinetics and pharmacodynamics of multiple
weekly subcutaneous efalizumab doses in patients with plaque psoriasis.
J. Clin. Pharmacol. 45, 286–298 (2005).
8. Charman, S.A., Segrave, A.M., Edwards, G.A. & Porter, C.J. Systemic availability
and lymphatic transport of human growth hormone administered by
subcutaneous injection. J. Pharm. Sci. 89, 168–177 (2000).
9. Bitonti, A.J. et al. Pulmonary delivery of an erythropoietin Fc fusion protein in
non-human primates through an immunoglobulin transport pathway. Proc.
Natl. Acad. Sci. USA 101, 9763–9768 (2004).
10. Baxter, L.T., Zhu, H., Mackensen, D.G. & Jain, R.K. Physiologically based
pharmacokinetic model for specific and nonspecific monoclonal antibodies
and fragments in normal tissues and human tumor xenografts in nude mice.
Cancer Res. 54, 1517–1528 (1994).
11. Ferl, G.Z., Wu, A.M. & DiStefano, J.J. 3rd. A predictive model of therapeutic
monoclonal antibody dynamics and regulation by the neonatal Fc receptor
(FcRn). Ann. Biomed. Eng. 33, 1640–1652 (2005).
12. Garg, A. & Balthasar, J.P. Physiologically-based pharmacokinetic (PBPK) model
to predict IgG tissue kinetics in wild-type and FcRn-knockout mice.
J. Pharmacokinet. Pharmacodyn. 34, 687–709 (2007).
13. Deane, R. et al. IgG-assisted age-dependent clearance of Alzheimer’s amyloid
beta peptide by the blood-brain barrier neonatal Fc receptor. J. Neurosci. 25,
11495–11503 (2005).
14. Weinstein, J.N. et al. The pharmacology of monoclonal antibodies. Ann. NY
Acad. Sci. 507, 199–210 (1987).
15. Mordenti, J. & Rescigno, A. Estimation of permanence time, exit time, dilution
factor, and steady-state volume of distribution. Pharm. Res. 9, 17–25 (1992).
art
16. Ng, C.M., Stefanich, E., Anand, B.S., Fielder, P.J. & Vaickus, L. Pharmacokinetics/
pharmacodynamics of nondepleting anti-CD4 monoclonal antibody (TRX1)
in healthy human volunteers. Pharm. Res. 23, 95–103 (2006).
17. Hayashi, N., Tsukamoto, Y., Sallas, W.M. & Lowe, P.J. A mechanism-based
binding model for the population pharmacokinetics and pharmacodynamics
of omalizumab. Br. J. Clin. Pharmacol. 63, 548–561 (2007).
18. Lammerts van Bueren, J.J. et al. Effect of target dynamics on pharmacokinetics
of a novel therapeutic antibody against the epidermal growth factor receptor:
implications for the mechanisms of action. Cancer Res. 66, 7630–7638 (2006).
19. Brambell, F.W., Hemmings, W.A. & Morris, I.G. A theoretical model of gammaglobulin catabolism. Nature 203, 1352–1354 (1964).
20. Junghans, R.P. Finally! The Brambell receptor (FcRB). Mediator of transmission
of immunity and protection from catabolism for IgG. Immunol. Res. 16, 29–57
(1997).
21. Waldmann, T.A. & Strober, W. Metabolism of immunoglobulins. Prog. Allergy
13, 1–110 (1969).
22. Ober, R.J., Radu, C.G., Ghetie, V. & Ward, E.S. Differences in promiscuity for
antibody-FcRn interactions across species: implications for therapeutic
antibodies. Int. Immunol. 13, 1551–1559 (2001).
23. Jin, F. & Balthasar, J.P. Mechanisms of intravenous immunoglobulin action in
immune thrombocytopenic purpura. Hum. Immunol. 66, 403–410 (2005).
24. Getman, K.E. & Balthasar, J.P. Pharmacokinetic effects of 4C9, an anti-FcRn
antibody, in rats: implications for the use of FcRn inhibitors for the treatment
of humoral autoimmune and alloimmune conditions. J. Pharm. Sci. 94,
718–729 (2005).
25. Mezo, A.R., McDonnell, K.A., Castro, A. & Fraley, C. Structure-activity
relationships of a peptide inhibitor of the human FcRn:human IgG interaction.
Bioorg. Med. Chem. 16, 6394–6405 (2008).
26. Hwang, W.Y. & Foote, J. Immunogenicity of engineered antibodies. Methods
36, 3–10 (2005).
27. Hakimi, J. et al. Reduced immunogenicity and improved pharmacokinetics
of humanized anti-Tac in cynomolgus monkeys. J. Immunol. 147, 1352–1359
(1991).
28. Kimball, J.A. et al. The OKT3 Antibody Response Study: a multicentre study of
human anti-mouse antibody (HAMA) production following OKT3 use in solid
organ transplantation. Transpl. Immunol. 3, 212–221 (1995).
29. Svenson, M., Geborek, P., Saxne, T. & Bendtzen, K. Monitoring patients treated
with anti-TNF-alpha biopharmaceuticals: assessing serum infliximab and antiinfliximab antibodies. Rheumatology (Oxford) 46, 1828–1834 (2007).
30. Stephens, S. et al. Comprehensive pharmacokinetics of a humanized antibody
and analysis of residual anti-idiotypic responses. Immunology 85, 668–674
(1995).
31. Schellekens, H. Immunogenicity of therapeutic proteins: clinical implications
and future prospects. Clin. Ther. 24, 1720–1740; discussion 1719 (2002).
32. Rehlaender, B.N. & Cho, M.J. Antibodies as carrier proteins. Pharm. Res. 15,
1652–1656 (1998).
33. Mordenti, J., Chen, S.A., Moore, J.A., Ferraiolo, B.L. & Green, J.D. Interspecies
scaling of clearance and volume of distribution data for five therapeutic
proteins. Pharm. Res. 8, 1351–1359 (1991).
34. Grene-Lerouge, N.A., Bazin-Redureau, M.I., Debray, M. & Scherrmann, J.M.
Interspecies scaling of clearance and volume of distribution for digoxinspecific Fab. Toxicol. Appl. Pharmacol. 138, 84–89 (1996).
35. Woo, S. & Jusko, W.J. Interspecies comparisons of pharmacokinetics and
pharmacodynamics of recombinant human erythropoietin. Drug Metab.
Dispos. 35, 1672–1678 (2007).
36. Vugmeyster, Y., Szklut, P., Tchistiakova, L., Abraham, W., Kasaian, M. & Xu, X.
Preclinical pharmacokinetics, interspecies scaling, and tissue distribution
of humanized monoclonal anti-IL-13 antibodies with different IL-13
neutralization mechanisms. Int. Immunopharmacol. 8, 477–483 (2008).
37. Duconge, J., Fernandez-Sanchez, E. & Alvarez, D. Interspecies scaling of the
monoclonal anti-EGF receptor ior EGF/r3 antibody disposition using allometric
paradigm: is it really suitable? Biopharm. Drug. Dispos. 25, 177–186 (2004).
38. Furuya, Y., Ozeki, T., Takayanagi, R., Yokoyama, H., Okuyama, K. & Yamada, Y.
Theory based analysis of anti-inflammatory effect of infliximab on Crohn’s
disease. Drug Metab. Pharmacokinet. 22, 20–25 (2007).
39. Meno-Tetang, G.M. & Lowe, P.J. On the prediction of the human response: a
recycled mechanistic pharmacokinetic/pharmacodynamic approach. Basic
Clin. Pharmacol. Toxicol. 96, 182–192 (2005).
40. Marathe, A., Peterson, M.C. & Mager, D.E. Integrated cellular bone homeostasis
model for denosumab pharmacodynamics in multiple myeloma patients.
J. Pharmacol. Exp. Ther. 326, 555–562 (2008).
41. Cartron, G. et al. Therapeutic activity of humanized anti-CD20 monoclonal
antibody and polymorphism in IgG Fc receptor FcgammaRIIIa gene. Blood 99,
754–758 (2002).
Clinical pharmacology & Therapeutics | VOLUME 84 NUMBER 5 | NOVEMBER 2008
557
state
art
42. Meijer, R.T., Koopmans, R.P., ten Berge, I.J. & Schellekens, P.T.
Pharmacokinetics of murine anti-human CD3 antibodies in man are
determined by the disappearance of target antigen. J. Pharmacol. Exp. Ther.
300, 346–353 (2002).
43. Mould, D.R. et al. A population pharmacokinetic-pharmacodynamic analysis
of single doses of clenoliximab in patients with rheumatoid arthritis. Clin.
Pharmacol. Ther. 66, 246–257 (1999).
44. Sharma, A. et al. Comparative pharmacodynamics of keliximab and
clenoliximab in transgenic mice bearing human CD4. J. Pharmacol. Exp. Ther.
293, 33–41 (2000).
45. Deng, R. & Balthasar, J.P. Pharmacokinetic/pharmacodynamic modeling of
IVIG effects in a murine model of immune thrombocytopenia. J. Pharm. Sci.
96, 1625–1637 (2007).
46. Mager, D.E., Mascelli, M.A., Kleiman, N.S., Fitzgerald, D.J. & Abernethy, D.R.
Simultaneous modeling of abciximab plasma concentrations and ex
558
47.
48.
49.
50.
vivo pharmacodynamics in patients undergoing coronary angioplasty. J.
Pharmacol. Exp. Ther. 307, 969–976 (2003).
Bauer, R.J., Dedrick, R.L., White, M.L., Murray, M.J. & Garovoy, M.R. Population
pharmacokinetics and pharmacodynamics of the anti-CD11a antibody
hu1124 in human subjects with psoriasis. J. Pharmacokinet. Biopharm. 27,
397–420 (1999).
Ng, C.M., Joshi, A., Dedrick, R.L., Garovoy, M.R. & Bauer, R.J. Pharmacokineticpharmacodynamic-efficacy analysis of efalizumab in patients with moderate
to severe psoriasis. Pharm. Res. 22, 1088–1100 (2005).
Luo, F.R. et al. Correlation of pharmacokinetics with the antitumor activity of
Cetuximab in nude mice bearing the GEO human colon carcinoma xenograft.
Cancer Chemother. Pharmacol. 56, 455–464 (2005).
Zhu, H., Baxter, L.T. & Jain, R.K. Potential and limitations of
radioimmunodetection and radioimmunotherapy with monoclonal
antibodies. J. Nucl. Med. 38, 731–741 (1997).
VOLUME 84 NUMBER 5 | NOVEMBER 2008 | www.nature.com/cpt