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3.4.2 Drug Metabolism andDrug–Drug Interactions
The simplest form of elimination is direct excretion of an unchanged drug molecule
into the urine, bile, or occasionally tears, sweat, or air. More commonly, molecules
undergo biotransformation, a process of metabolism that involves building or
breaking chemical bonds within the molecule to improve the body’s ability to
excrete it. Biotransformation is grouped into Phase I and Phase II reactions. Phase I
enzymes catalyze oxidations, reductions, and/or hydrolysis to introduce or unmask
functional groups in the molecule. Phase II enzymes conjugate endogenous small
polar molecules to the unmasked functional groups to inactivate the drug and
improve its water solubility for elimination. A drug may be subject to Phase I
metabolism, Phase II, or both. It is not uncommon that toxic products are formed
during this process. Sometimes, knowledge of a drug’s metabolism is exploited by
chemists to devise a prodrug, a molecule whose metabolism creates the true thera-
peutically active compound, to improve ADME properties.
The CYP family of integral membrane (endoplasmic reticulum, cell, and mito-
chondria) enzymes is composed of many related isozymes and catalyzes the metab-
olism of about 80% of currently used drugs. These enzymes are responsible for a
major portion of drug Phase I metabolism. CYP enzymes are expressed in all tissues
and are most abundant in the liver, small intestine, and kidney. For instance, the
CYP3A4 isoform is very abundant in the liver and intestinal epithelium and contrib-
utes to the biotransformation of almost one half of drugs, while CYP2D6 is one of
the least abundant isozymes and is involved in the metabolism of a quarter of all
drugs (Ekroos and Sjögren 2006).
Identifying which CYP isozymes are responsible for metabolism of the lead
compound, called reaction phenotyping, is important for two reasons. First, many
genetic polymorphisms have been identied for CYP isozymes. Polymorphisms
result from inherited differences in enzyme expression or mutations that alter
enzyme activity. These differences create variation in the rates of drug metabolism
within a patient population. Dosing regimens may need to be adjusted to properly
treat slow or ultra-fast metabolizers (see Sect. 3.5 on pharmacogenomics).
The second reason for reaction phenotyping is to understand the risk of drug–
drug interactions. Co-administration of certain drugs or dietary components can
impact the activity (inhibition) or abundance (induction) of the metabolizing
enzyme(s), etc. These interactions must be carefully screened for, as they can either
increase the clearance of concomitant medications, rendering them inactive, or
decrease elimination and thereby result in toxic exposures.
3.4.3 AI andInSilico Experiments
Algorithms, a form of articial intelligence (AI), have been developed to predict
absorption, distribution, metabolism, excretion, and toxicity (ADMET) based on
the structure of molecules (the inclusion of toxicity in ADME will be discussed
below). Hundreds of molecules can be interrogated for their drug-like properties in
D. Mochly-Rosen et al.
121
a short period of time. This is a fast, efcient, and resource-saving approach to
preselect molecules for chemical synthesis (Guan et al. 2019). The quality of
prediction needs to be spot-checked with real experiments for chemical series. AI
predicting drug–drug interactions, potential side effects, and the role of genetic
variation on drug response can now be integrated into the early stages of drug
discovery (see Sect. 2.6 on AI).
3.4.4 In Vitro Experiments
Initial studies of ADME characteristics are likely to be invitro due to the high cost
of animal studies. Although algorithms exist to extrapolate invitro data to living
systems, preliminary invivo studies should be performed to conrm that invitro
data are indeed predictive. If the results are in concurrence, a strategy of invitro
screening with limited invivo testing can be adopted. This approach allows more
rapid and cost-effective identication of compound liabilities and better selection of
a formulation before moving into animal models.
A number of different test systems are available to measure the invitro or intrin-
sic clearance (CL
int
), perform metabolite identication, and examine drug–drug
interaction liabilities. These are listed in Table3.2. CYP phenotyping is typically
done using cDNA (complementary DNA) expression systems (SUPERSOMES™).
In addition, a number of invitro and culture models are used for drug metabolism
(e.g., microsome assays) and to predict absorption via different routes of adminis-
tration (Caco-2 and MDCK cell lines, mucosal tissues, and skin).
3.4.5 In Vivo Experiments
The goal of invivo PK experiments is to calculate bioavailability, AUC, volume of
distribution, and half-life while validating clearance and metabolite identity data
collected from invitro studies. For xenobiotics, FDA requires safety studies in at
least two mammalian species, including one nonrodent species, typically the dog. A
single species might be sufcient for biologics. Pharmacokinetic, toxicology, and
Table 3.2 In vitro test systems for intrinsic clearance
Test system Specic models
cDNA CYP expression
systems
Single enzyme system (Phase I and II, FMOs, MAO’s, UGT,
NAT’s CESs)
Cell extracts S9 fraction (Phase I and II)
Microsomes (Phase I only)
Cell culture Hepatocytes (fresh or cryopreserved)
HepG2 cells transfected with CYP isozymes
Whole tissue Liver slices
Abbreviations: NAT Arylamine N-Acetyltransferase, CES Carboxylesterase, cDNA Complementary
DNA, CYP Cytochrome P450, FMO Flavin-Containing Monooxygenase, MAO Monoamine
Oxidase, UGT UDP-glucuronosyltransferase
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pharmacodynamic studies are essential to set the starting dose for rst in human
(FIH) dosing. Typically, physiological-based pharmacokinetic approaches are used
to predict the starting and efcacious doses for xenobiotics. For biologics, allometry
in concert with PK/PD modeling provides the rationale for FIH dosing. Because
upper dosing levels are usually set at the appearance of adverse side effects (or in
the case of oncology drugs, severe adverse effects), invivo pharmacokinetics studies
go hand-in-hand with toxicology studies. For this reason, invivo testing is often
described as ADMET studies, in which the T refers to toxicity assays, including
potential DNA damage and mutagenesis (Ames test), potential cardiac arrhythmia
(cardiac hERG inhibition), and carcinogenicity liabilities; the latter three are tested
in culture.
Rarely, a new molecule exhibits all the desired drug-like properties. In silico
coupled with invitro screening serve as a prelude to invivo PK studies. Prior to
initiating in vivo studies, predetermined criteria need to be met and conrmed
invitro. The single-dose PK (SDPK) design should aim to maximize the amount of
information gained. Thus, collection of urine in addition to plasma is recommended.
If the target is the central nervous system (CNS) or if CNS penetration is associated
with undesired effects, brain exposures should also be determined. Dosing routes
should include intravenous (IV) and the intended clinical route of administration,
often oral (po). Plasma sampling should commence 2min (IV) or 5min (po) post
dosing and continue for 24h to enable creation of a prole as shown in Fig.3.3.
Pay attention to the predicted ADME properties from invitro studies. Any dis-
connect warrants an examination and decision whether optimization can proceed
with predominantly invitro experimentations or can only be successful with invivo
screening. Emerging lead molecules should also be examined for biliary excretion
of parent compound and its metabolites. The compound may be secreted by the
glycoprotein 1 (permeability glycoprotein, also known as multidrug resistance
protein) into the intestinal lumen. The extent of its contribution to drug blood levels
can be assessed in bile duct cannulated rats by collecting feces. A dose escalation
study in the rodent toxicology species will establish the dose-limiting toxicity and
the dose-limited absorption. If human efcacious levels cannot be attained,
specialized formulations may help achieve the desired exposure.
3.4.6 The Bottom Line
Proper PK studies can help drug developers maximize their therapeutic window
between minimum efcacious dose and maximum tolerated dose. PK studies inform
how the lead compound is absorbed, distributed, metabolized, and excreted from the
body. In vitro PK testing is used to identify initial metabolism rates and routes, in
addition to identifying potential drug–drug interactions. In vivo PK testing is
essential for establishing a pharmacokinetic/pharmacodynamic relationship and the
maximum tolerated dose and therapeutic window in animals for a lead compound,
which becomes the basis for planning safe and effective doses to move into human
trials. Because different crystal forms, salts, and formulations of the same compound
D. Mochly-Rosen et al.
123
can have different ADME characteristics, it is very important to show favorable PK
properties before scaling up good manufacturing practice (GMP) production for
clinical trials to avoid costly reformulation delays.
3.5 Pharmacogenomics
CollenMasimirembwa
The term pharmacogenetics was coined in the 1960s to describe variability in drug
response due to heredity. In the post-human genome era, the term pharmacogenom-
ics appeared to capture the -omics approach with respect to biomarkers of drug
response. Variations in genes that code for drug metabolizing enzymes and drug
transporters affect the drug’s PK, whereas variations in genes that code for drug
targets inuence the drug’s PD.Ultimately, the effects on the PK or PD inuence
the individual’s response to a drug with respect to safety and efcacy or both.
Many drug–gene interactions (DGI) have been characterized and some success-
fully used to optimize treatment outcome. This has led to the general belief that
pharmacogenomics (PGx) is the low hanging fruit for genomics-based precision
medicine (Dere and Suto 2009). The biopharmaceutical industry initially panicked
at the perceived disruption of the traditional model of “one-drug ts all” model to
one of individualized medicine that PGx was dening for the future of medicine.
Cases like the antiretroviral drug efavirenz, which disproportionately caused an
avoidable burden of neuropsychiatric adverse effects in people of African ancestry
because of reduced metabolism, serve as a pointer that the pharmaceutical industry
can do better with PGx knowledge (Fig.3.4) (Masimirembwa and Dandara 2016).
Resources
• Rydzewski RM (2008) Chapter 9 – ADME and PK Properties. In: Real
World Drug Discov. Chem. Guide Biotech Pharm. Res. Elsevier,
pp353–429.
Box 3.8: What Surprised an Academic?
Demonstrating a clear drug-gene-interaction predisposing people of African
ancestry to efavirenz-induced adverse effects in 2008 was not enough! It took
another 10 years for the clinical implementation of this knowledge.
Unfortunately, this was too late for many patients whose lives were already
affected by the drug’s adverse effects. —CM
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Fig. 3.4 Efavirenz risk for neuropsychiatric adverse effects. Figure adapted from (Masimirembwa
and Dandara 2016). Abbreviations: ADRs Adverse Drug Reactions, PGx Pharmacogenomics, CYP
Cytochrome P450, CNS Central Nervous System, WHO World Health Organization, HAART
Highly Active Antiretroviral Therapy
3.5.1 Types ofGenetic Variations andTechnologies
forTheir Detection
The most common genetic variations in pharmacogenes are single nucleotide poly-
morphisms (SNPs). Although most polymorphisms are in noncoding regions or are
synonymous (causing no amino acid changes), some of the nonsynonymous varia-
tions result in functionally important changes. Variations can also be due to inser-
tions or deletions within the encoding genes, copy number variations that can result
in loss of function, or gene amplication resulting in increased function. Finally,
variations may also arise from epigenetic effects and post-translational
modications.
The best understood variations are in genes important for drug metabolism and
pharmacokinetics. It is therefore not surprising that most DGI highlighted in FDA
& EMA product labels and addressed by various clinical pharmacogenetics guide-
lines are for drug metabolizing enzymes. Whereas other genes can be important for
specic drugs, variations in CYP2C9, CYP2C19, and CYP2D6 are among the most
important for most drugs on the market. The Clinical Pharmacogenetics
D. Mochly-Rosen et al.
125
Implementation Consortium (CPIC) provides a list of DGIs for which there is very
strong clinical evidence (Level 1A) to support their application in the use of some
drugs to reduce risk for adverse effects and/or increase treatment efcacy (see Sect.
3.5 Resources).
Genetic variations in drug targets should also be considered. Here, in addition to
germline variations, somatic cell variations in genes coding for drug target pathway
proteins have gained prominence. Genetic variation in key signaling pathways in
cancer biology such as HER2, KRAS, EGFR, ALK, and BRAF can impact the ef-
cacy of many monoclonal antibodies such as trastuzumab, panitumumab, crizotinib,
and trametinib. Use of these drugs strongly depends on the availability of genetic
tests for the biomarkers since the mutation proles can change during the course of
disease progression and treatment, necessitating change of therapeutic agent combi-
nations to ones more likely to work in the evolving tumor (Shek etal. 2019).
Initially, PCR-restriction fragment length polymorphism (RFLP) methods were
used for the detection of PGx biomarkers, but have been replaced by reverse
transcription (RT)-PCR used by most molecular diagnostic laboratories, especially
in multiplex formats using open array and chips. This in turn will be replaced by
next generation sequencing (NGS) when it becomes less expensive. The choice of
genotyping platform depends on the application.
The use of SNP panels is very common since it is fast, easy to implement and
interpret, and inexpensive. Unfortunately, it is limited in coverage for other
variants—especially from different ethnic groups. Sequencing gene SNP panels
detects rare variants and is still relatively inexpensive, but is also limited to the
targeted genes on the sequence panel. Genome-wide genotyping, a more expensive
approach, is useful for population studies (Genome Wide Association studies
(GWAS)), but have the challenge of requiring large sample sizes to detect
statistically signicant drug–gene interaction associations. NGS enables the discov-
ery of novel variants, including variants of unknown clinical signicance (VUS)
that are not clinically actionable. To apply the ndings to patient care, PGx requires
analytical validation of the methods, performing tests in accredited laboratories, and
approval of tests by the relevant regulatory authorities (Arbitrio etal. 2021).
3.5.2 Pharmacogenomics inDrug Discovery andDeployment
In the drug discovery, development, and deployment value chain depicted in
Fig.3.5, pharmacogenomics now plays an important role. This is evidenced by the
numerous genomics research programs in biopharmaceutical and biotechnology
companies and by the long existing Industry Pharmacogenomics Working Group
(I-PWG) (Bienfait etal. 2021). The role of genetic variations in drug targets, which
has been demonstrated often in the development of anti-cancer drugs, and how the
study of these variations should be applied in the discovery phase or in patient
selection for clinical studies, is not discussed here.
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Fig. 3.5 The role of pharmacogenetics in the drug discovery, development, and deployment value
chain. Abbreviations: PGx Pharmacogenomics, ADME Absorption, Distribution, Metabolism, and
Excretion, ADRs Adverse Drug Reactions, HTS High-Throughput Screening, ID Identication
3.5.2.1 Late Discovery Phase
During lead optimization and drug candidate selection, standard invitro assays to
identify drug metabolizing enzymes and drug transporters that are involved in the
disposition of new chemical entities can be used and include cellular, subcellular,
and recombinant proteins (see Sect. 3.4 for more on ADME/PK). Identifying
metabolizing enzymes with genetic variability, such as CYP2B6, CYP2C9,
CYP2C19, and CYP2D6 alerts the drug discovery team to select other analogues
and/or to increase medicinal chemistry efforts to reduce the role of the polymorphic
enzymes. The team may also proceed with the compound, but further phase 1
clinical pharmacogenetic studies will be needed to quantify the pharmacokinetic
impact of the genetic variability on the specic compound.
3.5.2.2 Development Phase
If the ADME of a new chemical entity is affected by polymorphic genes, this should
be considered when designing the phase 1 study to allow careful evaluation of the
effects of the genetic variations. For example, if the compound mainly depends on
CYP2D6 for its metabolism, participants with a CYP2D6 poor metabolizer (PM)
genotype may be excluded or given a relatively low dose of the test compound. Such
stratication prevents or reduces the occurrence of severe adverse drug reactions
(ADRs) and can contribute to better clinical trial outcomes, thus reducing
attrition rates.
If PGx studies are included in phase 1 and phase 2 studies, much failure related
to both lack of efcacy and/or toxicity can be avoided. PGx documentation in phase
3 should be included along with other confounding factors, such as different
co-morbidities, co-treatments, different age groups, and importantly different
population ancestry groups. It has now become standard to biobank DNA samples
D. Mochly-Rosen et al.
127
from clinical trials, as this affords industry an opportunity to investigate potential
genetic factors to explain clinical trial observations beyond those that could have
been anticipated from phase 1 and 2 PGx studies. Regulatory agencies such as FDA
and EMA have provided recommendations regarding how to submit
pharmacogenomic data in the New Drug Application (NDA) dossier (Sect. 3.5
Resources). This information helps the regulators with product label information.
Over 150 products on the market now have some PGx information and/or clinical
guidelines (Sect. 3.5 Resources).
Although regulatory authorities have provided PGx product label information,
extensive clinical studies and evidence evaluation is needed to inform clinical
guidelines on how to implement PGx in the clinical setting. Many consortia of
experts have been developing such guidelines. Notable ones include CPIC, the
Royal Dutch Association for the Advancement of Pharmacy—Pharmacogenetics
Working Group (DPWG), the Canadian Pharmacogenomics Network for Drug
Safety (CPNDS), and the French National Network of Pharmacogenetics (RNPGx).
The PharmGKB database then keeps an accessible summary of these guidelines on
its website (Sect. 3.5 Resources).
There are now clinical guidelines for over 148 drugs on the market. Guidelines
from different consortia are not always in agreement, and there are ongoing efforts
to harmonize them to ensure their generalizability. Unfortunately, product labels
and clinical guidelines are mainly based on DGI data obtained in populations of
European ancestry, with increasing data on Asian populations, but very little data on
people of African ancestry, since very little clinical PGx research has been done in
Africa (Radouani et al. 2020). Even the most commercially available PGx tests,
such as for CYP2C9, do not test for the African-specic variants, such as CYP2C9*5,
*6, *8, *11; therefore, results from those tests are not predictive for dosing of com-
mon drugs such as warfarin or siponimod in people of African ancestry. The same
is true for CYP2D6*17 and *29, for which African-specic variants are not included,
resulting in poor dose predictions for African populations. To address this decit in
data and knowledge, a number of programs have been initiated in Africa, such as
iPROTECTA (implementing pharmacogenetic testing for effective treatment and
care in Africa) that use PGx tests such as GenoPharm that are inclusive of variants
unique to African populations (Mbavha etal. 2022). These studies are inspired by
the highly successful PREPARE program in Europe (van der Wouden etal. 2020)
and IGNITE in America (Weitzel etal. 2016).
These global programs are also working to address barriers in clinical implemen-
tation of PGx such as knowledge, attitudes, and practices (KAP), ethical legal and
societal issues (ESI), laboratory facilities, electronic patient record systems, and
clinical decision support systems (CDSS) for point-of-care application of PGx
(Roosan etal. 2021). Whether the deployment of clinical PGx uses a pre- emptive or
a reactive approach, it should result in safer and more effective use of medicines and
fewer failures in drug development (Fig.3.6).
Integrating PGx in national pharmacovigilance programs can be an invaluable
tool to improve safety and efcacy for medicines with PGx issues as they are
deployed across the world. For example, analyzing ADR data deposited in the
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Fig. 3.6 The anticipated impact of clinical pharmacogenetics on the use of some medicines if
adopted as the standard of care. Abbreviations: ADRs Adverse Drug Reactions, PGx
Pharmacogenomics
World Health Organization (WHO) database, Vigibase, shows that for some drugs,
different populations report different risk for ADRs (Ampadu etal. 2016). Such
approaches could help regulatory agencies identify medicines that require bridging
pharmacokinetic and pharmacogenetic studies in different population groups.
The Bottom Line
• Pharmacogenomics (PGx) is the eld that studies variations in genes that
code for drug metabolizing enzymes and drug transporters.
• PGx plays an important role in drug development. If ignored, it can put
good discovery programs at risk for failure in later stages of development.
It is therefore highly recommended to integrate PGx testing early in drug
development.
• When drugs are approved, barriers in clinical implementation of PGx
include lack of knowledge, incorrect attribution to cultural differences
rather than genetics, and ethical, legal, and societal issues. Other barriers
for applying PGx information for better patient care include lack of
laboratory facilities, challenges with electronic patient record systems, and
lack of clinical decision support at the point of care.
• Unfortunately, drug labels and clinical guidelines are mainly based on
drug–gene interaction data obtained in populations of European ancestry,
with increasing data on Asian populations, but very little data on people of
African ancestry.
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3.6 Route ofAdministration andDrug Formulation
TerrenceF.Blaschke
The route of administration and the formulation of a drug are dependent on the
chemical composition of the drug and the desired onset and duration of action. The
route of administration of a drug can be broadly separated into three categories: (1)
enteral, (2) parenteral, and (3) topical (Table3.3). In each of those categories, there
are a number of subcategories.
Each of these routes of administration requires a different type of formulation.
Many companies are developing drug delivery technologies involving oral, nasal,
inhalation, transdermal, and parenteral delivery platforms.
3.6.1 Oral Route
The most common, desirable, and usually the least expensive route of administra-
tion is the oral route, especially if the drug is intended for multiple doses or chronic
administration. However, for many drugs, the oral route may not be feasible or
practical, as the drug may have poor oral bioavailability and not reach adequate
concentrations in the systemic circulation. For the oral route, there are many differ-
ent forms (tablet, capsule, liquid, suspension, etc.) chosen based on the convenience
of the formulation and the bioavailability of the drug.
Another important characteristic of an oral formulation is its rate of absorption.
In some settings, rapid absorption is desirable to achieve a rapid onset of action
Box 3.9: What Frustrated an Academic?
Not all drugs reach their target when delivered in a simple formulation. Proper
formulation and route of delivery is also critical when using new
pharmacological agents for basic research, whether in culture or invivo. It is
important to include studies on drug stability and distribution for each
formulation of a new pharmacological agent. —DM-R
Resources
• CPIC Gene-Drug Interactions: https://cpicpgx.org/genes- drugs/.
• FDA Guidance for industry, pharmacogenomic data submission docu-
ment: https://www.fda.gov/media/122944/download.
• FDA Table of Pharmacogenomic Biomarkers in Drug Labeling: https://
www.fda.gov/drugs/science- and- research- drugs/table- pharmacogenomic-
biomarkers- drug- labeling.
• PharmGKB Clinical Guideline Annotations: https://www.pharmgkb.org/
guidelineAnnotations.
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