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12 • Recent Advances in PK/PD 311
that they are binding to. This is not true for biotherapeutic modalities, and as such,
target‑mediated drug disposition (TMDD) can be a major clearance mechanism [11].
Binding of biotherapeutics to soluble targets can also act as a signicant sink for drugs.
Also, the development of anti‑drug antibodies to biotherapeutics can result in acceler‑
ated clearance of these drugs. TMDD, binding to soluble target sinks, and immuno‑
genicity can restrict the concentration of drug available to exert its pharmacological
effects [11,12]. In turn, heterogeneity of receptor expression on cells and the number of
cells expressing the target can lead to variability in both PK and PD [12]. As such, it is
of great importance to understand the PK/PD relationship of biotherapeutic drugs.
In addition, biotherapeutics display complex biodistribution, driven by their size
[13,14]. Small molecules tend to have relatively rapid distribution driven by diffusion
across membranes, which enables plasma concentrations to be used as a surrogate of tis‑
sue concentrations. Antibodies, cell therapies, and gene therapy vectors are much larger
in size, and their diffusion across membranes is restricted. Their distribution is driven
by processes, including extravasation and non‑specic or receptor‑mediated endocyto‑
sis. As a result, tissue concentrations are not in rapid equilibrium with plasma concen‑
trations. In addition, many biotherapeutics have distal sites of action. As a result, there is
often a need to predict tissue concentrations and relate these to pharmacological effects.
Many biotherapeutic modalities, including gene therapies, have intracellular mech‑
anisms of action, providing a greater level of complexity. Often, data from disparate
sources need to be integrated to understand the impact of multiple downstream pro‑
cesses. These can have multiple non‑linearities leading to non‑intuitive results. QSP
modeling is ideal to tackle these and can be used to deconvolve complex mechanisms
of action. In the next section, different biotherapeutic modalities will be introduced and
their specic PK and PD considerations and challenges will be discussed.
12.2 PK/PD CHARACTERISTICS AND
CONSIDERATIONS FOR BIOTHERAPEUTICS
12.2.1 Monoclonal Antibodies (mAbs)
Ever since the rst FDA approval of a therapeutic monoclonal antibody (mAb) targeting
CD3 in the 1980s, there has been a tremendous increase, especially in the last decade
or two, in the discovery and development of biotherapeutics, including mAbs, and more
recently novel modalities such as cell and gene therapies. With signicant clinical suc‑
cesses and over 100 regulatory approvals for a variety of indications, including cancer
and auto‑immune diseases, biotherapeutics such as mAbs are changing treatment para‑
digms and are increasingly dominating R&D portfolio programs of large pharmaceu‑
tical and biotechnological companies for various therapeutic areas [15]. In addition,
advances in antibody engineering technologies have enabled the successful develop‑
ment of novel format antibodies like ADCs, bispecic or multispecic Abs, antibody
fragments, recycling Abs, and sweeping Abs. Such therapeutic modalities have several
312 Biopharmaceutical Informatics
advantages as compared to conventional small molecules, including high potency, better
target selectivity, less non‑specic activity, and better half‑lives [16–20].
Most therapeutic mAbs are of IgG (immunoglobulin G) format, which usually
consists of two Fab fragments involved in antigen binding and target recognition and
one Fc region involved in binding to various cell receptors such as FcRn (receptors
mainly expressed on vascular endothelium and hematopoietic cells) and FcγR (receptors
expressed on immune cells). With a much higher molecular weight (~150 kDa) and size
(~14 nm) as compared to that of small molecules (usually less than 0.9 kDa and 1 nm in
molecular weight and size, respectively), PK processes, including absorption, distribu‑
tion, metabolism, and excretion (ADME), are quite different and unique for mAbs as
compared to traditional small molecules [16–20]. For instance, glomerular ltration, tis‑
sue distribution, and cellular penetration are generally limited for large molecules such
as mAbs. Rather, their PK and distribution are mainly governed by mechanisms such as
pinocytosis for non‑specic cellular uptake, extravasation, intracellular catabolism as
a major pathway of elimination, target‑mediated clearance, and salvage via FcRn recy‑
cling, which contribute to their longer half‑life in the systemic circulation and perhaps
limited tissue distribution [16,17].
The Fc region of the Ab binds to the FcRn receptors only at acidic pH with no or
minimal binding at neutral pH in the blood. After pinocytosis, mAbs enter the early
endosome where they bind to FcRn at pH 6. The Fc‑FcRn complex protects the Abs
from lysosomal degradation and is eventually recycled back from the endosomes to the
cell membrane. Once at the cell surface, the Fc‑FcRn complex dissociates due to weak
binding at neutral pH, and eventually, Abs are released back into the systemic circula‑
tion, which contributes to their longer half‑life [21–23]. Signicant efforts have been
carried out to further modulate the half‑life of mAbs with the help of specic amino
acid mutations in the Fc region of the Ab. Specic mutations can either increase or
decrease the binding to the FcRn and thereby increase or decrease the half‑life. Also,
reducing the charge or isoelectric point (pI) of the mAbs can also increase their half‑life
given that the negative cell surface charge can cause repulsion with a negatively charged
Ab and decrease pinocytosis [17,24]. Besides, the Fc region is also involved in engaging
with the host immune system via FcγR expressed on various effector cells and mediat‑
ing PD effects of mAbs. Most Abs mediate PD effects through one of the following
mechanisms – neutralizing a target, suppressing a pathway, and either enhancing or
suppressing immune effector function.
In addition to FcRn‑mediated mechanisms, the PK of mAbs is also strongly inu‑
enced by its binding to the specic target, where it can undergo rapid clearance due to
target‑mediated endocytosis, the phenomenon commonly known as TMDD. Here, an
Ab binds to the target, and the Ab‑target complex rapidly internalizes and gets cleared
from systemic circulation through protein catabolism. In contrast to FcRn recycling,
which is non‑specic and generally not saturable at usual doses of Ab, TMDD is capac‑
ity‑limited and saturable, which is impacted by the dose of Ab, levels of the target,
turnover of the target, and binding afnity of the Ab toward the target [25].
One of the MoAs for therapeutic mAbs is to neutralize the soluble target, which
is pathogenic in nature. However, high dose levels and/or frequency of Abs and
Ab‑mediated target accumulation are major associated challenges. While research
12 • Recent Advances in PK/PD 313
groups have explored increasing the exposure and decreasing the dosing frequency of
Abs via enhanced FcRn binding afnity, the challenges associated with target accumu‑
lation remain. Other antibody formats such as recycling and sweeping antibodies have
also been explored [26,27]. Recycling antibodies bind to the target in a pH‑dependent
manner with decreased binding at pH 5.5–6 as compared to neutral pH. As a result,
once they enter the endosomes after pinocytosis, the target antigens are released from
the complex for lysosomal degradation, while the Abs are salvaged via FcRn recy‑
cling. Sweeping antibodies also leverage improvements, including increased FcRn
binding afnity and enhanced uptake of the Ab‑target complex into the endosomes.
Advancements in Ab engineering have led to the successful development of novel anti‑
body formats, including bispecics and multispecics, which possess unique PK/PD
attributes and considerations as compared to conventional Abs. For instance, a bispe‑
cic Ab has two binding domains, each specic to a different target and simultaneous
engagement to form a trimolecular complex mediating PD effects. Such novel formats
have the additional advantages of increased potency and reduced chances of resistance
due to dual targeting. One such class of drugs is T‑cell engagers for oncology with
several molecules approved and many in early‑ and late‑stage clinical development
[12,18,28]. The bispecic T‑cell engager (BiTE) molecules were the rst‑generation
candidates with novel Ab formats without Fc and much smaller molecular weight and
size as compared to full IgGs. Hence, their half‑lives were much shorter as compared
to conventional Abs. Next‑generation molecules were half‑life extension (HLE) with
Fc‑fusion proteins to improve half‑life and reduce dosing frequency. Plenty of other
Ab formats are leveraged for this class of drugs (e.g., bivalent or biparatopic) to further
improve potency and specicity. Thus, each of these novel formats of Abs can have its
own unique PK/PD characteristics and considerations.
Due to complex mechanisms and unique PK/PD characteristics, various PK/PD
modeling and simulation (M&S) approaches are used in the discovery and development
of therapeutic Abs [16,17]. Abs can typically exhibit either linear PK or non‑linear PK.
Linear PK properties are typically governed by non‑specic mechanisms such as pino‑
cytosis and FcRn recycling, which are usually not saturable at relevant doses of Abs.
Such molecules display dose proportionality for exposure, and other PK attributes such
as clearance and half‑life are independent of the dose level. Linear PK proles after IV
administration of mAbs are typically biphasic (distribution and elimination) in nature
where typical two‑compartmental PK models can be used. Various preclinical species
have been previously explored to characterize the PK of Abs and translate to humans.
Among them, cynomolgus monkeys have been shown to translate best for the PK of
mAbs in humans after using allometric scaling for the two species [29]. This can be
attributed to the similar FcRn binding afnity for IgGs in the two species, which is known
to be different for other species such as rodents. Other human FcRn transgenic mouse
models such as Tg32 and Tg276, which express human FcRn, have also been explored as
potential alternative models to characterize the PK of Abs, especially for rank‑ordering
candidates for PK, and to estimate linear PK parameters for non‑cross‑reactive mol‑
ecules [30]. In addition to linear PK characteristics, Abs can also exhibit non‑linear PK
characteristics usually attributed to target binding and impacted by target levels, target
turnover, and binding afnity. Such molecules do not display dose proportionality for
314 Biopharmaceutical Informatics
exposures, and a clear impact of doses on clearances and half‑lives is typically observed.
In order to quantitatively characterize the non‑linearity, empirical Michaelis‑Menten
(MM) or semi‑mechanistic TMDD modeling approaches are utilized [31].
While MM approach uses a constant, Km, and the maximum rate of non‑linear elimina‑
tion, Vmax, as non‑linear parameters, TMDD approach leverages target binding afnity
(equilibrium dissociation constant, KD; association rate constant, Kon; and dissociation
rate constant, koff), target concentrations, and target turnover (synthesis rate, ksyn; and
elimination or internalization rate, kdeg/int) as related parameters. While the former
approach is more empirical, knowledge of the interspecies differences for the different
parameters can be included while characterizing non‑linearity with the latter approach.
Additional complex, mechanism‑based PBPK models for mAbs have also been devel‑
oped with the incorporation of measurable physiological (system‑based) and drug‑spe‑
cic parameters [32]. More specically, such models incorporate parameters related to
plasma and lymph ow rates, rate of pinocytosis, lysosomal degradation, FcRn recy‑
cling, recirculation ow rate, lymphatic/vascular reection coefcients, tissue volumes,
and others. PBPK‑based modeling approaches are extremely useful to characterize tis‑
sue distribution of mAbs (e.g., to brain or ocular compartments), triage drug candidates
for lead selection, and leverage in vitro and in vivo data to apriori predict the distribution
of Ab candidates to specic tissues and sites of action. As with PK, various modeling
approaches are also used to characterize the PD of mAbs across different species and
translate to humans. For instance, empirical, indirect response models have been used
previously for oncology indications, where tumor static concentrations (TSCs) are esti‑
mated from preclinical mouse xenograft studies and used for efcacious dose projec‑
tions in the clinic [33]. However, such empirical approaches typically do not account
for interspecies differences on several parameters, including target expression or levels,
binding afnity, and tumor dynamics. To address these gaps and given the complex
MoA of Abs, more mechanistic QSP models have also been explored to characterize the
PD of Abs and translate from preclinical species to humans [34].
12.2.2 Antibody‑Drug Conjugates (ADCs)
Antibody‑drug conjugates (ADCs) are a class of targeted therapies for cancer treatment
that combine a specic antibody to a tumor antigen linked to a potent cytotoxic agent.
The aim of this therapeutic is to target the cytotoxic drug (known as the payload) to
tumor cells, thus maximizing efcacy while minimizing systemic toxicity. ADCs are
clinically validated, with 14ADCs currently FDA approved for various solid and hema‑
tological malignancies [35]. Following distribution into the tumor, ADCs bind to an
over‑expressed antigen on the surface of tumor cells. The ADC is internalized into the
cell, and the payload is released in the endosomal or lysosomal compartment (via differ‑
ent mechanisms). The payload can then diffuse or be transported into the cytosol, where
it can bind to its intracellular target, which triggers tumor cell killing. Alternatively,
some ADCs (e.g., EDB‑ADCs) rely on extracellular cleavage releasing membrane per‑
meable payloads [36]. There are different types of payload classes, including microtu‑
bule inhibitors, DNA cross‑linkers, and topoisomerase inhibitors, which are all potent
12 • Recent Advances in PK/PD 315
cytotoxins. One potentially important aspect of the ADC mechanism is the ‘bystander
effect’, whereby the cytotoxic drug released in the targeted cell can diffuse out of that
cell and into other (non‑target‑expressing) tumor cells to exert its cytotoxic effect. This
is important as solid tumors tend to be heterogeneous and not all cells in a tumor will
express the targeted protein.
ADCs have been very successful, demonstrating transformative responses in the
clinic, and consequently are one of the fastest growing classes of anticancer drugs.
However, they have a complex mechanism of action, with many variables which need
to be optimized to enable optimal delivery of the payload to express its cell‑kill‑
ing pharmacology. In addition, they are not truly targeted and can be taken up into
non‑malignant cells releasing their payload. As such, ADCs have been limited by sev‑
eral challenges in the clinic, including sub‑optimal efcacy and dose‑limiting toxicities.
To understand the variables to be optimized, the complex disposition of ADCs must be
delineated. Following administration of ADCs into the systemic circulation, they can
deconjugate or be catabolized, which releases the payload. They can also bind to healthy
cells expressing target or to soluble (shed receptors), both of which can be signicant
drug sinks. ADC clearance can therefore be a combination of rst‑order elimination
and TMDD. The distribution of ADCs into tumors occurs mainly via paracellular pas‑
sage across blood capillaries. Given the large size of ADCs, this is a slow process and
often results in incomplete penetration of ADCs into the center of tumors. This can be
exacerbated by the binding‑site barrier effect, whereby binding of the ADC to the cells
close to the blood supply of the tumor, and subsequent internalization, restricts the ADC
from distributing deeper into the tumor [37]. In contrast, the payload has PK properties
consistent with a small‑molecule drug. As such, the released payload can diffuse out of
cells into the extracellular space and into the systemic circulation where they tend to
be rapidly cleared, limiting general tissue toxicity. In addition, payloads can be actively
transported out of tumors by efux transporters, which can limit the intracellular expo‑
sure to the cytotoxic drug. The combination of large (e.g. antibody) and small molecules
(e.g. linker, payload) in an ADC contributes to their complex ADME properties.
The inherent complexity of ADCs lends itself well to the use of mathematical mod‑
eling and simulation, to map out the mechanism of action and to consider the impact of
multiple variables. Several mathematical models have been published for ADCs over
recent years, evolving from empirical and semi‑mechanistic PK/PD models, toward
more mechanism‑based models [38]. PK/PD models have proven very useful in the
preclinical and clinical development of ADCs to maximize information obtained from
experimental data, while minimizing resource utilization. These models have been used
to establish in vitro to in vivo correlation of ADC efcacy [33], quantify and trans‑
late from in vivo studies to the clinic [39], and differentiate between ADCs binding
to the same target [40]. However, they are limited in their ability to predict efcacy
across different targets and to inform design parameters. QSP models contain sufcient
mechanistic details to enable an understanding of the processes critical to an ADC’s
performance and to perform multiscale predictions. These models describe cellular
mechanisms, tumor penetration, preclinical to clinical translation, and clinical simula‑
tions [38]. A seminal paper by Shah and coworkers presented a bench‑to‑bedside trans‑
lation of brentuximab vedotin using a multiscale QSP model [41]. This model provided
316 Biopharmaceutical Informatics
translation from preclinical experiments to humans to successfully predict clinical out‑
comes for brentuximab vedotin. A similar model structure and translational strategy
was also applied by others for the successful prediction of clinical outcomes for T‑DM1
[42,43] and inotuzumab ozogamicin [44]. Further details are provided in the case stud‑
ies (section 4). There are fewer publications on QSP models for ADC toxicity [45,46],
which is a denite gap in the science.
12.2.3 Cell Therapies
Cellular immunotherapy or adoptive cell therapy (ACT) includes tumor‑inltrating
lymphocytes (TILs), engineered T‑cell receptor T cells (TCR‑T), chimeric antigen
receptor T cells (CAR‑Ts), chimeric antigen receptor natural killer cells (CAR‑NK),
and other cell types. Here, patients are usually subjected to autologous or allogeneic
cell therapies, where on autologous therapy, they are rst subjected to leukaphere‑
sis, and immune cells are isolated, often transduced ex vivo, expanded to large num‑
bers, and then infused back for treatment. In contrast, allogeneic cell therapies are
‘off‑the‑shelf’ and derived from normal healthy donors or other cell sources. The
expanded and genetically modied immune cells better interact with the target on the
tumor cells, in an MHC‑dependent or independent manner (depending on the specic
type of cell therapy), which subsequently leads to the induction of key signaling events
such as immune cell activation and proliferation, cytokine production, and eventu‑
ally tumor cell lysis [47,48]. Currently, there are several approved autologous CAR‑T
therapies targeting CD19 in hematological malignancies and B‑cell maturation antigen
(BCMA) for multiple myeloma patients [49]. These therapies have resulted in unprec‑
edented clinical outcomes for relapsed/refractory terminally ill patients; hence, there
has been an exponential increase in research for the development of cell‑based immu‑
notherapies, especially for the treatment of cancer.
Cellular therapies like CAR‑Ts exhibit unique pharmacokinetics (PK) that differ
greatly from conventional therapeutics. The PK proles of small or large molecules
would typically capture the processes of ADME. However, some of these aspects are
not directly applicable to novel modalities like CAR‑Ts. In contrast to small or large
molecules, CAR‑Ts undergo rapid proliferation or expansion after infusion in patients;
thus, both total cells infused and those expanded in vivo are quantitated while charac‑
terizing the PK. Hence, they are considered as ‘living biologics’ or ‘replicating thera‑
peutics’ and their in vivo kinetic disposition is termed as ‘cellular kinetics’ (CK) [50].
As shown in Figure12.1, a typical CK prole of CAR‑T therapy is multiphasic
and includes four distinct phases, including margination or distribution, expansion, con‑
traction, and persistence [50]. Margination or Distribution: After administration in
patients, CAR‑Ts rapidly disappear from the bloodstream within a few hours and get
extensively distributed in peripheral tissues (e.g., lung, spleen, lymph, and bone mar‑
row), which corresponds to the margination or distribution phase. Expansion: Upon
target antigen recognition and engagement, CAR‑Ts undergo rapid proliferation and
expansion along with the release of cytokines and subsequent killing and clearance of
the tumor cells. The expansion phase is followed by biexponential decline of CAR‑Ts
12 • Recent Advances in PK/PD 317
FIGURE12.1 A typical cellular kinetics (CK) prole for CAR-Ts demonstrating multi-phasic
nature and key CK parameters. Cmax, maximum observed concentration; Tmax, time of
Cmax; AUC0-28d, area under the concentration-time curve from time zero to 28 days after
dosing; AUC0-84d, area under the concentration-time curve from time zero to 84 days
after dosing; Clast, last quantiable concentration; Tlast, time of last quantiable concentration. Figure adapted from [50].
that are termed as contraction and persistence phases. Contraction: The contraction is
likely due to loss of antigen stimulation upon tumor clearance. Other likely mechanisms
include T‑cell exhaustion or activation‑induced cell death (AICD). Persistence: The low
levels of CAR‑Ts in the persistence phase are thought to represent the memory‑type cells
which sustain for longer periods of time in the patient’s body. Overall, the CK prole
of CAR‑Ts is described by two general sets of PK parameters to capture the ‘expan‑
sion’ and ‘persistence’ phases: Cmax (maximal/peak expansion), Tmax (time to reach
Cmax), and AUC (area under the curve) over a shorter period (e.g., AUC
) are used to
0–28d
318 Biopharmaceutical Informatics
describe the expansion phase, while Clast (last measurable concentration), Tlast (time of
last measurable concentration), and half‑life are used to capture the ‘persistence’ phase
of CAR‑Ts [50].
The multiphasic CK prole of CAR‑Ts is attributed to how the CAR‑Ts get dis‑
tributed in patients post‑infusion and how they interact (MOA) with the tumors and
patient’s immune system. Monitoring CK in preclinical and clinical studies can
help better understand CAR‑T in vivo expansion and persistence and dose‑expo‑
sure‑response correlation. However, signicant variabilities have been reported
across different clinical trials and the dose‑exposure‑response relationship is often
convoluted for CAR‑T therapies. Typically, there is a lack of clear correlation between
dose and exposure as well as dose and response (safety or efcacy) for CAR‑Ts [51,52].
In contrast, CAR‑T expansion (Cmax and AUC
with responses in clinical studies. Since cytokine release is anticipated with this
MOA, expansion is also linked with adverse events or toxicity, including cytokine
release syndrome (CRS). Besides, a variety of factors, including patient‑related (e.g.,
tumor burden) or product‑related (e.g., CD4:CD8 or immune phenotype), can poten‑
tially impact the CK prole and overall clinical performance (safety and efcacy) of
CAR‑Ts [51,52].
Typical PK compartmental models that have been traditionally utilized are not
applicable for CK of CAR‑Ts. Non‑compartmental analysis (NCA) and/or model‑based
approaches (NLME model with piecewise function) along with covariate analysis are
typically used to characterize CK, as well as evaluate the dose‑exposure‑response
(safety and efcacy) relationship and impact of various covariates. Besides, various
mechanistic, PBPK, and QSP modeling approaches have been developed for CAR‑T
therapy that have been extensively summarized elsewhere along with their potential
applications [51–54].
) has been shown to correlate well
0–28d
12.2.4 Gene Therapies
Gene therapy is a form of treatment that involves the delivery of genetic material to
cells, in order to modify the expression of a specic gene or set of genes. The goal of
gene therapy is to treat or prevent diseases that are caused by genetic mutations or de‑
ciencies [55]. There are several different types of gene therapy, including gene replace‑
ment therapy, gene editing, and gene silencing.
More than a thousand clinical experiments have been conducted with gene therapy
since it was rst developed more than 20 years ago [55]. Nonviral vectors have appeal‑
ing characteristics for clinical use, but they are inefcient in vivo, prompting future
advancements in these vectors. It is important for vector development and optimization
to understand how nonviral vectors behave within cells. The model‑based approach is
an effective tool for comprehending and describing the various mechanisms that gene
transfer systems should overcome inside the body. A model‑based approach enables the
known use of PK/PD modeling in conventional therapeutics [55].
PK considerations for gene therapies include how the therapeutic gene is deliv‑
ered to the target cells, the efciency of the delivery method, and the stability of
12 • Recent Advances in PK/PD 319
the therapeutic gene within the target cells [56]. The most common delivery method
for gene therapy is the use of viral vectors. These are viral particles that have been
genetically modied to carry the therapeutic gene into the target cells. Some of the
most commonly used viral vectors for gene therapy include adenoviruses, adeno‑
associated viruses (AAVs), and lentiviruses. Each of these viral vectors has its own set
of advantages and disadvantages, and the choice of vector will depend on the specic
disease being targeted and the characteristics of the therapeutic gene [56]. Once the
therapeutic gene has been delivered to the target cells, it must be expressed in order for
the therapy to be effective. The level of gene expression will depend on a number of
factors, including the efciency of the delivery method, the stability of the therapeutic
gene within the target cells, and the activity of the therapeutic gene. Factors that can
affect the stability of the therapeutic gene include the presence of epigenetic modica‑
tions, such as methylation or histone modication, and the activity of the cell’s DNA
repair machinery [56]. Additionally, the therapeutic gene may be subject to degradation
by cellular enzymes, such as nucleases.
PD considerations include the level of expression of the therapeutic gene within the
target cells, the duration of gene expression, and the safety and efcacy of the therapy in
treating the target disease [57]. The safety and efcacy of the therapy will depend on the
specic disease being targeted and the nature of the therapeutic gene. Gene replacement
therapy involves the delivery of a functional copy of a gene that is missing or mutated
in a patient with a genetic disorder [57]. This type of therapy is typically used to treat
diseases that are caused by a single‑gene mutation, such as cystic brosis or hemophilia.
In contrast, gene editing involves the precise modication of a specic gene or set of
genes. This type of therapy is typically used to treat diseases that are caused by a spe‑
cic genetic mutation, such as sickle cell anemia or Tay‑Sachs disease. Meanwhile, gene
silencing involves the inhibition of the expression of a specic gene or set of genes. This
type of therapy is typically used to treat diseases that are caused by the over‑expression
of a specic gene, such as cancer [51,57].
Other considerations for gene therapies include the potential for off‑target effects,
the risk of mutagenesis, and the potential for immune response to the viral vector or
therapeutic gene. Additionally, the specic disease being targeted will also play a role in
the design and development of a gene therapy. For example, the PK/PD considerations
for a gene therapy used to treat a chronic disease like cancer will be different than for a
gene therapy used to treat a genetic disorder like cystic brosis [57].
12.2.5 Vaccines
A vaccine is a biological preparation that provides active acquired immunity to a par‑
ticular disease. The goal of a vaccine is to prevent infection by stimulating the immune
system to recognize and ght the pathogen that causes the disease. There are several
different types of vaccines, including inactivated, live attenuated, subunit, and DNA/
RNA vaccines [58].
For vaccines, PK factors include the route of administration, the dosage, and the
schedule of administration. The most common routes of administration for vaccines
320 Biopharmaceutical Informatics
include injection (subcutaneous, intramuscular, and intradermal) and oral. The choice
of route will depend on the specic vaccine, the population being vaccinated, and the
disease being targeted [58,59]. Some vaccines, such as live attenuated vaccines, may
only be administered via one route, while others, such as subunit vaccines, may be
administered via different routes. The dosage and schedule of administration are also
important PK considerations. The dosage will depend on the specic vaccine and the
population being vaccinated. Infants, for example, may require a different dosage than
adults. Some vaccines, such as the measles, mumps, and rubella (MMR) vaccine, may
require two or three doses for full protection, while others, such as the tetanus vaccine,
may only require a single dose [59].
Immunogenicity refers to the ability of the vaccine to stimulate an immune response.
The immunogenicity of a vaccine will depend on the specic vaccine and the popula‑
tion being vaccinated. Some vaccines, such as live attenuated vaccines, may be highly
immunogenic, while others, such as inactivated vaccines, may be less immunogenic.
Safety of the vaccine is an important consideration, as the vaccine should not harm the
person receiving it [58,59]. Adverse effects of the vaccine are expected but should be
minimal and acceptable for the benets the vaccine provides.
Efcacy refers to the ability of the vaccine to protect against the disease. The
efcacy of a vaccine will depend on the specic vaccine and the population being
vaccinated. Some vaccines, such as the human papillomavirus (HPV) vaccine, may
be highly effective in preventing infection, while others, such as the inuenza vac‑
cine, may be less effective. The efcacy of a vaccine is also dependent on the cover‑
age rate, i.e., the percentage of the population that receives the vaccine; the more the
coverage, the more effective the vaccine is in controlling the spread of the disease
[59]. Vaccines include the potential for off‑target effects, the risk of mutagenesis,
and the potential for immune response to the viral vectors or therapeutic genes [60].
Additionally, the specic disease being targeted will also play a role in the design and
development of a vaccine. For example, a vaccine for a highly contagious disease like
measles will have different PK/PD considerations than a vaccine for a less contagious
disease like hepatitis B [58–60].
In conclusion, PK/PD characteristics and considerations for vaccines are complex
and multifaceted and require careful study and optimization in order to develop safe
and effective vaccines. The route of administration, dosage, schedule of administra‑
tion, immunogenicity, safety, and efcacy are all important factors to consider when
developing a vaccine [58]. Additionally, the specic disease being targeted, the popula‑
tion being vaccinated, and the potential for off‑target effects, mutagenesis, and immune
response must be considered.
12.2.6 mRNA/siRNA/Oligonucleotide Therapeutics
mRNA, siRNA, and oligonucleotide therapeutics are based on the use of nucleic acids
to modulate gene expression and have the potential to treat a wide range of diseases,
including genetic disorders, cancer, and viral infections [61,62].