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Antidrug responses to mAbs usually result in accelerated clearance of the drug. Antidrug:
mAb complexes tend to be large, multimeric complexes due to the bivalent nature of both
drug and ADA molecules and the large, multimeric complexes are rapidly cleared (Johansson
et al., 2002; Montero-Julian et al., 1995; Rojas et al., 2005; van der Laken et al., 2007). Antidrug
complexes formed with therapeutic proteins or peptides are mo re variable, and can form Ab:
therapeutic protein complexes, which may or may not be neutralizing, and may provide a
type of circulating depot, which can actually increase the circulating concentrations and dura-
tion of exposure for the therapeutic protein or peptide (Rosenblum et al., 1985; Working,
1992; Genzyme, 2010). Formation of these complexes can change the response profiles in un-
expected ways depending upon the balance between duration of exposure and degree of
neutralization (Working, 1992).
Confirmation of the presence of ADAs is crucial to correct interpretation of observed PK
profiles and evaluation of drug exposure (Swann et al., 2008; Tabrizi and Roskos, 2007).
Further characterization, including identification of circulating forms, epitope mapping and
evaluation of neutralization of activity may also be necessary for correct interpretation of
PD. Immunogenicity assessments also require assays, which have been carefully developed
and fully characterized (see Bioanalytics).
Pharmacokinetics and pharmacodynamics
Quantitative characterization of the PK is a crucial par t of discovery and development and
therapeutic application of biotherapeutics. It helps selecti ng a candidate with desirable PK
properties. It forms the basis for characterizing the nonclinical exposure-response/safety rela-
tionship to project clinical dose range and dosing schedule. It is fundamental in understand-
ing and optimizing clinical exposure-response and exposure-safety relationships, to
understand the impact of covariates and need for dose adjustments in subgroups of patients
or under certain cond itions.
Modeling and simulation have become an integral part of drug development (Milligan
et al., 2013). Integration of available data into models and continuous refinement throughout
the life cycle of drugs, or model informed drug discovery and development (MID3) have
evolved to become the industry standard (Marshall et al., 2016, 2019; Wang et al., 2019).
0 14 28 42 56 70 84
0.0001
0.001
0.01
0.1
1
10
100
0
200
400
600
800
1000
1200
1400
**
*
Time (days)
Bound Cytokine Concentration
(pg/mL)
*
*
*
FIGURE 5.5 Serum concentrationetime
profiles of an anti-cytokine mAb (triangles)
and bound cytokine (circles) for three indi-
vidual monkeys. IV bolus dose ¼ 0.4 mg/kg.
Bound cytokine is target engagement marker.
Samples with asterisks were confirmed anti-
drug antibody (ADA)-positive. ADA-
positive animal had parallel drop in bound
cytokine levels.
5. Novel biopharmaceuticals: Pharmacokinetics, pharmacodynamics, and bioanalytics146
I. Development of biopharmaceuticals
PK methods
Several methods of varying detail and complexity are used to quantitatively characterize
exposure and its relationship with response.
PK analyses for biopharmaceuticals (and small molecules) often rely on simple, descrip-
tive, noncompartmental methods (Meibohm and Braeckman, 2008). Noncompartmental
methods are widely used due to their simplicity, ease of use, and minimal assumptions, espe-
cially for early molecule screening studies and toxicokinetic exposure evaluations. However,
some of the basic assumptions of the noncompartmental methods and standard compart-
mental models, particularly elimination from a “central compartment” (blood) and rapid
equilibrium between blood and tissues (peripheral compartments) are not correct for most
biopharmaceuticals. Distribution volumes, in particular, are not estimated correctly by non-
compartmental methods (Lobo et al., 2004). Noncompartmental methods should be used for
limited exposure evaluations, and preferably only for molecules showing linear, dose-
proportional PK, where target interactions likely have no or minimal impact.
Compartmental PK modeling offers advantages over noncompartmental analysis. It de-
scribes the time course of exposure with finer detail, allows simulation of conditions
(doses, schedules, time points) for which no data are available, and evaluation of multiple
models to characterize nonlinear and linear clearance components. Compartmental PK
models are commonly used in population PK analysis during clinical development, using
nonlinear mixed effects as a method to characterize the typical PK profile in the population
and interindividual and other sources of variability. The linear PK characteristics of mono-
clonal antibody therapeutics are most commonly described by a two-compartment model
with elimination from the central compartment, consistent between species (Betts et al.,
2018).
Physiologically based (PB) PK models describe drug disposition in multiple compartments
representing physiological rather than empirical compartments. The advantage of PBPK
models over empirical compartmental models is that they allow characterizing exposur e in
physiological compartments of interest, including characterization of local target expression
and TMDD. PBPK models typically have more parameters, which may not all be identifiable
from plasma PK observations only. They are therefore used most often for predictive rather
than descriptive purposes, with applications in the nonclinical, translational space. Clinical
applications include assessment of potential for DDI (e.g., Xu et al., 2015) and scaling from
adult to pediatric populations (Gill and Jones, 2022). Generic PBPK model frameworks
have been published to characterize the disposition of mAbs in common nonclinical species
and in human. Baxter et al. (1995) described a full body PBPK model to scale antibody bio-
distribution from mouse to human, including blood and lymph flows, specific and aspecific
binding, extravasation through small and large pores and metabolism. Shah and Betts (2012)
expanded on this and other generic models, among others by adding (sub)cellular compart-
ments, FcRn recycling and providing parameterization for mouse, rat, monkey and human.
Cao et al. (2013) reduced the generic model to the minimum required to capture mAb PK
behavior, des cribing the flow of antibody from blood through tight and leaky tissue compart-
ments into lymph. This minimal PBPK model retains key advantages of physiology based
models (model definition in terms of physiologically interpretable parameters, the possibility
Pharmacokinetics and pharmacodynamics 147
I. Development of biopharmaceuticals
to add multiple processes for binding and elimination) while sharing with empirical compart-
mental models the need to estimate only few parameters (Cao et al., 2013; Zhao et al., 2015).
TMDD is an essential element in PK models for many biotherapeutics. Models to describe
TMDD range from simple MichaeliseMenten to a full description of receptor synthesis and
degradation, antibody association, dissociation and internalization of the Ab-receptor com-
plex, receptor shedding and the fate of the soluble receptor-Ab complex. The range of
TMDD models was comprehensively reviewed by Dua et al. (2015).
Whatever the method of PK evaluation, the interpretation is critically dependent upo n the
bioanalytical methods used to generate the drug concentration data (see Drug Assays). A
clear understanding of the specificity of the drug assay (free drug, bound drug, total drug,
partial fragments) is especially critical.
Representative PK for molecule types
PK parameters for mAbs have been summarized in a number of reviews (Wang et al.,
2008; Lobo et al., 2004; Mould et al., 1999; Deng et al., 2012). Limited reviews are available
for therapeutic proteins and pept ides (Tang et al., 2004; Mahmood and Green, 2005) and
data are available for individual molecules or molecule types (Foote, 2008; Ryff et al., 2008;
Beals et al., 2008; Marian and Oeswein, 2008; Modi, 2008; Lazarus and Wagener, 2008;
Sam, 2008; Yang and Kido, 2011; Evans et al., 2011; Davies et al., 2011). Selected example
serum concentration profiles for mAbs, therapeutic proteins, peptides, and modified mole-
cules are provided in Figs. 5.2, 5.5, and 5.6. Table 5.2 lists clearance mechanisms, parameter
values for clearance, half-life and bioavai lability of example biotherapeutics referenced in this
chapter. Unmodified mAbs typically have slow clearance and long terminal half-lives (days
to weeks) (Table 5.2). Parameters for antibody fragments overlap the range of values for ther-
apeutic proteins and peptides, reflecting similar molecule size and clearance pathways. Clear-
ance is more rapid and half-lives are shorter (minutes to hours) and more variable for
therapeutic proteins and peptides. Bioavailability following SC or IM administration is highly
0 24 48
1
10
100
1000
10000
Time (hr)
Serum Concentration
(ng/mL)
8470564228140
Time (days)
FIGURE 5.6 Comparative serum
concentrationetime profiles for an
mAb (MW w150 kDa) (circles), thera-
peutic protein (cytokine MW
w34 kDa) (squares), and peptide
(incretin hormone MW w3 kDa) (tri-
angles) following a single IV bolus
administration in monkeys. Data
normalized to 100 mg/kg equivalent
doses. Inset profile for mAb to 84 days
postdose shows extended duration of
exposure typical of mAbs.
5. Novel biopharmaceuticals: Pharmacokinetics, pharmacodynamics, and bioanalytics148
I. Development of biopharmaceuticals
variable with biopharmaceuticals. mAbs, therapeutic proteins, and peptides, in general, have
moderate to high bioavailability (w30% to 100%) (Wang et al., 2008; Lobo et al., 2004; Tang
et al., 2004).
Pharmacodynamics, PK/PD methods and examples
Pharmacodynamics (PD) is the quantitative study of the relationship between drug expo-
sure (concentrations or dose) and pharmacologic or toxicologic responses. PK/PD analysis
combines PK and PD model components to describe the doseeconcentrationeresponse
time course. PK/PD models are especially useful for biopharmaceuticals since dose- and
time-dependent effects on PK and respo nses are common. PK/PD models for bio-
pharmaceuticals (and small molecules) have become increasingly sophisticated, and newer
mechanistic PK/PD models not only empirically describe the data, but can include pertinent
aspects of physiology, which allow extrapolation across species and disease indications. PK/
PD models can also provide simulations and hypothesis testing of potential drug impacts on
biology and can be of great value in early molecule design and engineering, particularly for
“biobetter” molecules where improvements in specific molecule characteristics (i.e., stability,
improved FcRn binding) or target interactions (i.e., improved affinity, different binding
epitope) are desired.
Model types and examples
Reviews of concepts and usage of PK/PD methods in quantitative pharmacology are pro-
vided by Meibohm and Braeckman (2008), Agoram et al. (2007), and Meibohm and Deren-
dorf (1997). The most familiar form of this model is a sigmoid E
max
model. Direct models
have been used to describe anti-IgE effects on free IgE concentrations (Racine-Poon et al.,
1997) (drug and effect site are both plasma) and IL-10 effects on ex vivo cytokine release
from leukocytes (Radwanski et al., 1998) (drug in plasma, target on surface of cells in plasma).
The responses for most biopharmaceuticals are usually not mediated by direct, rapid inter-
actions, since the effect sites are often remote from the blood, and/or there are a series of
transduction steps, which delay the responses. Maximum observed responses may not occur
when maximum drug concentrations are present in blood, and responses may be observed
even after drug can no longer be detected in blood. Indirect response models address this
temporal disconnect by modeling a balance of two processes: one process to produce the ef-
fect and a second process to decrease the effect (Dayneka et al., 1993). Indirect response
models have been a popular method for analysis since species- or disease-specific molecular,
or cellular physiologic processes can be included to facilitate nonclinical to clinical extrapola-
tions. Indirect response models have been used to evaluate anti-IL-5 effects on eosinophils
(Zia-Amirhosseini et al., 1999), GH effects on IGF-I (Sun et al., 1999), and anti-factor IX
and factor IX activity (Benincosa et al., 2000).
Mechanism-based, physiologic PK/PD models are the next step in model evolution from
empirical, descriptive methods to models, which can incorporate the physiology underlying
PK/PD relationships (Danhof et al., 2008; Mager et al., 2003). Mechanistic models can include
quantitative expressions characterizing the processes, which occur between plasma/serum
drug concentrations and effects, such as target distribution, target kinetics, target activation,
Pharmacokinetics and pharmacodynamics 149
I. Development of biopharmaceuticals
and in vivo response (transduction). Multiple types of mechanistic models have been devel-
oped, including cell lifespan models for many of the hematopoietic therapeutic proteins (Ago-
ram et al., 2006; Ramakrishnan et al., 2004; Perez-Ruixo et al., 2005), complex models with
feedbacks (pegfilgastrim effects on granulocytes (Yang and Kido, 2011; Roskos et al., 2006),
cetrorelix antagonism of lutein izing hormone (LH) release (Nagaraja et al., 2003)), and models
that link key cellular processes to both drug and biomarker levels (cellular regulation of bone
turnover, serum N-telopeptide and anti-RANK-L concentrations) (Marathe et al., 2011; Lem-
aire et al., 2004). Lindauer et al. (2017) used a nonclinical compartmental PK model combined
with a physiological tumor compartment that included a semimechanistic description of PD-1
turnover and tumor growth inhibition in response to target engagement by the PD-1 target-
ing antibody pembrolizumab. The translational model was used to augment initial clinical
observations in selection of expansion dose selection. Li et al. (2022) described the formation
of ternary complexes within the tumor between epcoritamab, CD3 on T cells and CD20 on
(malignant) B cells as a basis for understanding biomarkers, antitumor activity and safety
to support selection of the therapeutic dose and schedule.
Further evolution of mechanistic PK/PD modeling led to quantitative systems pharma-
cology (QSP), a discipline involving computational methods to characterize the complexity
of biological syste ms underlying disease and modulation of those systems by pharmaceutical
intervention. Since its introduction in an NIH white paper in 2012 (Sorger et al., 2011), the
discipline has steadily gained influence in drug discovery and development of small mole-
cules and biotherapeutics alike (Gadkar et al., 2016; Azer et al., 2021). Significant contribu-
tions have been made to biotherapeutics development in particular in complex disease
areas including (immuno)oncology (e.g., Coletti et al., 2020; Ma et al., 2020; Wang et al.,
2021; Kumar et al., 2021), immunology (e.g., Rogers et al., 2021; Coto-Segura et al., 2023),
neurodegenerative diseases (e.g., Geerts and Spiros., 2020; Markovic et al., 2023) and meta-
bolic disorders (e.g., Khaksar Toroghi et al., 2021). Integration of artificial intelligence is
emerging as a natural next step in the advancement of pharmacometrics in general (Chatur-
vedula et al., 2019) and QSP modeling in particular (Zhang et al., 2022; Terranova et al., 2023).
Nonclinical to clinical translation
The ultimate goal of nonclinical evaluations is to provide useful predictions of the new,
novel molecule’s potential behavior in humans and to establish initial clinical dosing regi-
mens for first-in-human (FIH) studies. Translation of the nonclinical information into pro-
posed clinical dosing strategies may involve integration of in vitro pharmacology, in vivo
pharmacology, PK, and PD across species and disease states (Agoram et al., 2007; Danhof
et al., 2008; Tabrizi et al., 2010; Lowe et al., 2010).
Steps to establish the FIH dosing regimen include proje cting human PK, establishing pe-
ripheral (and tissue, if possible) target drug concentrations and/or receptor engagement
necessary for activity or efficacy, evaluating potential maximum exposures, which are accept-
able for safety, then proposing appropri ate dosing regimens (dose levels, routes, regimens).
5. Novel biopharmaceuticals: Pharmacokinetics, pharmacodynamics, and bioanalytics150
I. Development of biopharmaceuticals
Human PK projections
Several methods are being used to predict human PK for biopharmaceuticals (Wang et al.,
2016; Pasquiers et al., 2022). Allometric scaling is the most widely used method and has been
reasonably successful for molecules with linear PK and lacking significant target-mediated
disposition (Wang and Prueksaritanont, 2010; Ling et al., 2009; Mahmood, 2009; Deng
et al., 2011; Dong et al., 2011b). Allometric scaling of therapeutic proteins and peptides typi-
cally uses a range of species (Mahmood, 2004). Human PK projections for mAbs can use two-
species scaling based on rodent and nonhuman primate (NHP) PK data (Wang et al., 2016;
Betts et al., 2018; Pasquiers et al., 2022), although use of NHP data only is most common
(Wang and Prueksaritanont, 2010; Ling et al., 2009; Deng et al., 2011; Dong et al., 2011b).
Greater similarities between NHP and human IgG interactions with Fc receptors, compared
with other species, may be partly responsible for the superior estimates from the NHP data.
In recent years however, human FcRn transgenic mice have become a popular alternative,
with predictions of human PK reported to be at least as accurate as those from NHP
(Avery et al., 2016). Population PK methodology has also been applied to allometric scaling
for some molecules, especially when dose-dependent, nonlinear PK was observed in the pre-
clinical studies (Dong et al., 2011b). Physiologically based pharmacokinetic (PBPK) modeling
approaches, which allow incorporation of additional biological data across species (i.e., dif-
ferences in target expression, drug affinities for the target, tissue exposure, FcRn binding,
etc.), can also provide estimates for molecules governed by target-mediated disposition
(Urva et al., 2010; Pasquiers et al., 2022).
Exposure response: Determining minimum and maximum targets for FIH
dosing
The US Food and Drug Administration (FDA) guidance for esti mating the maximum safe
starting dose (US Department of Health and Human Services, 2005) is the general method
used for selecting doses for FIH studies with biopharmaceuticals. The no-observed-
adverse-effect level (NOAEL) or highest nonseverely toxic dose (HNSTD) determined from
safety studies is converted to a human equivalent dose (HED), reduced by a safety factor
of at least tenfold to obtain the maximum recommended starting dose (MRSD). The pharma-
cologically active dose (PAD) is estimated based on preclinical in vitro and in vivo data. In
case the PAD is lower than the MRSD, it may be preferred as a basis for defining the FIH
starting dose. In addition, high-risk biopharmaceuticals are also required to estimate a min-
imum anticipated biologic effect level (MABEL) to further define potential initial starting
doses for any studies in the EU (European Medicines Agency, 2017). The maximum dose
to be evaluated should generally be aimed at characterizing the upper end of the PAD range.
The target concentrations proposed for clinical ef ficacy are typically derived from in vitro
evaluations with human cells and tissues and from in vivo studies in animal efficacy or ac-
tivity models. Transgenic animal models are commonly used if the biotherapeutic lacks reac-
tivity toward the animal target. Immunodeficient animal models or animal surrogate
molecules are sometimes considered when exposure cannot otherwise be maintained due
to immunogenicity. Optimal translation of exposure-response data from nonclinical models
to humans is dependent upon the selection of appropriate relevant animal models of efficacy
Nonclinical to clinical translation 151
I. Development of biopharmaceuticals
or activity with pharmacology similar to the human disease, and identification of measurable
response markers, which are relevant to the intended clinical disease pathobiology and can be
translated in a quantitative, meaningful way from animals to humans (Tabrizi et al., 2009b).
Animal activity and efficacy models typically recapitulate only limited aspects of human dis-
ease pathobiology, and it is critically important to understand those limitations. However,
in vivo animal activity or efficacy models can often demonstrate or confirm some aspects
of modulation of relevant target pathway activity, target engagement, and tissue targeting.
In addition, in vivo studies provide important informatio n on drug pharmacokinetics, espe-
cially disease or target impacts on PK, which can greatly reduce risk in human PK projections.
In the rare scenarios where no animal model of activity of efficacy is available, the in vitro
human evaluations, normal animal PK/PD, and historical information on human PK/PD
for similar biopharmaceuticals, if available, can be used for translation of target efficacious
concentration for humans.
Mechanistic PK/PD models, which can integrate drug-specific and biology-specific infor-
mation across species or diseases, are the newest and most effective method for predicting
drug concentrations required for clinical studies (Danhof et al., 2008; Yu et al., 2011; Gibbs,
2010). Mechanistic PK/PD models based on no nclinical data have been used to predict hu-
man concentration responses for anti- DKK-1 mAbs (Betts et al., 2010), anti-CD11a (Bauer
et al., 1999), anti-CD-4 (Zheng et al., 2011), and rhEPO (Ramakrishnan et al., 2004). Mecha-
nistic population PK/PD models have also bee n developed and may provide improved pre-
dictions, especially in situations with high interanimal variability and TMDD (Lowe et al.,
2010; Betts et al., 2010). PK/PD models are also the recommended method to evaluate
maximum safe exposure (NOAEL) or MABEL for high-risk biopharmace uticals (Yu et al.,
2011).
Bioanalytics
Bioanalytics method development and implementation for biopharmaceuticals have
unique challenges. Assays for biopharmaceuticals need to isolate and accurately quantitate
molecules (analytes), which are contained in a biologic matrix composed of often high con-
centrations of very similar molecules. The complexity of the matrices requires highly specific
reagents to capture the analyte or procedures to physically extract and isolate the analyte. The
analyte molecules are also complex in composition and structure, and are potentially fragile
outside their biological environments. Bioanalytical methods need to maintain integrity and
activity of the molecules to provide useful information. Samples supporting nonclinical PK/
PD and safety evaluations are primarily from in vivo studies, but can also include samples
from ex vivo or in vitro studies. Samples can be in a variety of biological matrices, including
blood, plasma, serum, synovial fluid, cerebrospinal fluid, organ lavages, tissue lysates, and
cell culture supernatants. Assays used in nonclinical development to support PK/PD analysis
typically include drug or surrogate molecule assays, target engagement or biomarker assays,
and ADA assays.
Immunoassays have been the traditional analysis platform for biopharmaceutical s for de-
cades and remain the most utilized methodology. However, LC-MS is playing an increas-
ingly significant role in bioanalytics for biopharmaceuticals, especially peptides and
5. Novel biopharmaceuticals: Pharmacokinetics, pharmacodynamics, and bioanalytics152
I. Development of biopharmaceuticals
therapeutic proteins. Considerations for selecting an assay platform include the type of ana-
lyte, expected samples matrices, required assay performance, especially minimum detection
limit, availability of reagents, costs, historical comparability needs, potential assay validation
requirements, and operational plans (i.e., outsourcing to vendors). A general comparison of
the performance characteristics of the major platforms is provided in Table 5.3.
TABLE 5.3 Assay platform comparisons: instruments, sensitivity, and range of assays.
Platform
Detection and
analysis instruments Sensitivity (LOQ) Range References
Traditional ELISA Spectrophotometer 10 pg/mL 10 pg/mLe1
ng/mL
Damen et al., 2009;
Nowatzke et al.,
2011
ECL MSD plate reader <1 pg/mL 1 pg/mL
e<100 ng/ mL
Meso Scale
Discovery, 2024
AlphaTechnology/
AlphaLisa
Fluorescent plate
reader
pg/mL range 2.5e5 logs PerkinElmer, 2024
Gyros GyroLab xPlore Similar to ECL 2e3 logs Gyros, 2024
RIA Gamma counters 2e3pM 2e1000 pM EMD Millipore, 2024
8 pg/mLe1 ng/mL 8 pg/mLe100
ng/mL
Low pg/mL for
ultrasensitive methods
(e.g. insulin)
2e3 logs
Immuno-PCR Real-time quantitative
PCR (RTqPCR)
reader
Low fg/mL Up to 5 logs Adler et al., 2008;
Spengler et al., 2009
LC-MS conventional
intact molecules
(<10 kDa)
ESI, MALDI, ITD,
FT-ICR, TOF
1e10 mg/mL 1e10 000 mg/
mL
Ackermann and
Berna, 2007; Ezan et
al., 2009;
Nowatzke et al.,
2011
LC-MS/MS conventional
with digestion; surrogate
peptide, MRM
ESI, MALDI, TQMS,
ITD, TOF
100e500 ng/mL 0.1 ng/mL
e1000 mg/ mL
Ackermann and
Berna, 2007; Ezan et
al., 2009;
Nowatzke et al.,
2011
LC-MS/MS immunocapture,
surrogate peptide, MRM
ESI, MALDI, TQMS,
ITD, TOF
<100 pg/mLe1
ng/ mL
0.1e10 000
ng/mL
Ackermann and
Berna, 2007; Ezan et
al., 2009;
Campbell and Le
Blanc, 2011;
Nowatzke et al.,
2011
Bioanalytics 153
I. Development of biopharmaceuticals
Method development guidances and regulations
The first meetings and workshops to discuss bioanalytic method development issues spe-
cific to biopharmaceuticals began in the early 1990s. An initial workshop led to a follow-up
series commonly known as Crystal City Bioanalytical Workshops (Nowatzke et al., 2011;
Shah and Bansal, 2011). The Ligand Binding Assay Bioanalytical Focus Group (LBABFG),
a joint industry, regulatory and academic group, was established at the second AAPS/
FDA Workshop on Bioanalytical Method Validation in January 2000. The focus group coor-
dinated a series of white papers on method validation (Findlay et al., 2000), PK assay support
(DeSilva et al., 2003; Miller et al., 2001; Smolec et al., 2005), ADA evaluation (Mire-Sluis et al.,
2004; Shankar et al., 2006; Gupta et al., 2007; Koren et al., 2008; Shankar et al., 2008), and
biomarker bioanalysis (Lee et al., 2005, 2006). Current regulatory guidance for bioanalytical
methods to support human clinical studies and nonclinical PK, pharmacology and toxicology
studies in the US is based on International Council for Harmonisation of Technical Require-
ments for Registration of Pharmaceuticals for Human Use (ICH) guideline M10 (US Depart-
ment of Health and Human Services, 2022). As of early 2024, ICH M10 is in the process of
being implemented in the EU.
Methods and platforms
Immunoassays
Immunoassay methods, by definition, measure the concentration of analytes through
antigeneantibody reactions utilizing at least one reagent, which is an antibody specificto
the analyte. The assay reagent antibodies serve to selectively capture the analyte and/or
generate a detection signal (Figs. 5.7e5.9). The assay can utilize basic colorimetric, fluores-
cent, electrochemiluminescent, or other detection methods. Depending on specific method
protocols, immunoassays can be performed in a timeframe of a few hours or up to 2 days.
Their strength lies in the inherent specificity of the molecular recognition of epitopes on
the analyte to be quantified by the reagent antibodies, thus having the ability to provide great
assay sensitivity and specificity without the need for extensive sample pretreatment or
enrichment of the molecule to be detected. Technology improvements have led to substantial
reductions in sample volume needs. Current platforms include the traditional enzyme-linked
immunosorbent assays (ELISA) and newer electrochemiluminesence (ECL) assays as well as
fluorescent-based assays (e.g., Gyros platform). Assays are run in a variety of plates including
standard 96-well, miniaturized 384- or 1536-well and compact disc (CD) formats. The newer
assay methods and larger sample formats have increased assay throughput, reduced assay
run-times, and improved assay performance with regard to minimum detection levels and
assay precision.
The ELISA platform is a colorimetric assay. It relies on labeled enzymes to catalyze reac-
tions, which generate a color or luminescence response. The response is then quantitated in
an optical reader. ELISAs typically employ an initial capture step, followed by washes of un-
bound analyte and sample matrix, and a detection step. ELISA reagents and hardware are
readily available in many formats, affordable, and supported by a wide variety of suppliers
and manufacturers. ELISA limitations include the requirement for large sample volumes
5. Novel biopharmaceuticals: Pharmacokinetics, pharmacodynamics, and bioanalytics154
I. Development of biopharmaceuticals
(50e200 mL), multiple wash steps, and moderate to long assay run-times (often involving an
overnight preparation plus 4e6 h actual assay time).
Radioimmunoassays (RIAs) were developed in the 1960s and are declining in use because
of operational concerns regarding radioisotope usage. They are still used, however, especially
for peptide quantitation, because of their extremely low picomolar (pM) detection limits, and
because it is sometimes useful to obtain data for the purposes of comparison with historical
data from previously approved peptide drugs (EMD Millipore, 2024). RIAs are used for drug
and biomarker assays. They are also used in competition assays where the analyte of interest
is radiolabeled with 125I, and coincubated with the analyte containing sample and an anti-
analyte antibody to initiate a competition reaction for the antibody-binding sites. The bound
analyte is subsequently separated from free analyte and quantitated using a standard curve.
Because of the competition nature of the assay method, the amount of analyte in the sample is
FIGURE 5.7 Immunoassay formats for mAb drug assays including generic, antigen capture, and anti-idiotype
antibody capture. (a) Generic antibody drug assay makes use of molecular handles for capture and detection that
are not unique to a specific antibody but can be used for entire classes of like molecules (e.g., detection of human/
humanized antibodies in rodent matrices using anti-human Fc and anti-human Fab antibodies). (b) Antigen-specific
capture introduces drug selectivity by capitalizing on the exclusive nature of the antibodyetarget recognition. (c) As
programs mature, fully selective capture and detection reagents such as anti-idiotype anti-drug antibodies are often
developed, thereby eliminating the need for any broad anti-framework molecules and facilitating adaptation of assay
across species (preclinical to clinical transition).
Bioanalytics 155
I. Development of biopharmaceuticals
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