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simvastatin at the dose of 160 mg/day decreased low-density lipoprotein (LDL) level
in 95% cases only, however, no LDL decline was observed in remaining 5% cases
(Davidson et al. 1997). The inter-individual dosage-dependent variation has been
associated with ABCG2 (ATP-binding cassette subfamily G membrane 2), and
HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) (Chasman et al.
2004; Tomlinson et al. 2010).
2.3 Factors Leading to Variation in Inter-Individual Drug
Response
The specific response of an individual to a drug depends on a variety of multifold and
intricate factors (Fig. 2.4) that include unique genetic composition (mutations like
gene deletions, gene duplications, and SNPs). Such genetic factors either alone or
together with physiological factors (gender, ethnicity, age, and body size), environ-
mental factors (smoking, toxin exposure, and diet), pathological conditions (obesity,
renal and hepatic function, and diabetes) affect drug response (Akhondzadeh, 2014).
Kalow et al. (1998) suggest that different genetic factors help in determining about
20–95% heterogeneity in inter-individual drug response. Moreover, genetic factors-
related individual drug response variability.
is usually irreversible, whereas the effects due to other factors are often transitory
(Ma and LU 2011). Vesell (1989) reported greater population drug response
variability in all individuals of the population compared to intra-patient variability
Fig. 2.4 Factors responsible for inter-individual drug response variation
2 Single Nucleotide Polymorphisms and Pharmacogenomics 33
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at occasions. This finding supports inheritance as the key determinant of drug
response.
2.4 Inter-Individual Drug Response Determinants
Specific medical disorders were diagnosed based on symptoms that may represent
multiple illnesses or linked to family background. In past years, physicians may just
strive to prevent or manage illness at its commencement (Akhondzadeh et al. 2009).
More specific techniques are now developed for the diagnosis of genes and genetic
variants believed to relate to altered inter-individual drug reaction or disease
conditions and in this area HGP completion has made significant contribution.
Pharmaco-genomics helps researchers to determine unique gene variants which
could be responsible for the specific drug reaction of a person via the detecting the
involved specific genetic loci (Ferrara 2007).
In recent years’ microarrays or biochips have been utilized in the technique of
genetic studies such as whole genome SNP profiling, gene expression, multi-gene
analysis, and haplotyping (Borges et al. 2015; Siest 2015), for determining the drug
response of individuals at different levels that may well help in the discovery and
development of drugs (Nair 2010).
Gene polymorphisms can affect the drug impact by either modifying its pharma-
cokinetics or pharmacodynamics or both, and these are the two key factors that
confer inter-individual variations in drug response. Pharmacokinetics determines the
required concentration of a drug to arrive at a specific target, whereas pharmacody-
namics analyzes the level of respond of different targets including ion channels,
enzymes, and receptors to a variety of drugs (Pirmohamed and Park 2001;
Pirmohamed 2014). The pharmacokinetic and pharmacodynamic properties of either
drugs utilized or their metabolic products or both are altered at the target sites due to
polymorphisms in the drug transporter and phase-I drug-metabolizing enzyme genes
that result in the variability of drug response. Variations even in a single-base (SNPs)
or closely linked cluster of SNPs (haplotypes) of genes participating at any level in
the pathways of drug pharmacokinetics and pharmacodynamics may, in principle,
influence an individual’ s overall drug responses (Eichelbaum et al. 2006; Lin 2007).
2.5 Application of SNPs in Clinical Trails
The development of tailored medication is guided by SNPs. The key information
obtained from gene polymorphisms may be utilized for deciding right drug doses.
The inter-individual genetic variation makes drugs well effective in some individuals
and ineffective in others (Roden et al. 2011). This gene polymorphism-based
variability in drug reaction may be utilized in the clinical of novel drugs. In the
beginning of clinical trial the evaluation of the functional association of SNPs may
help in selecting the individuals physio-genetically suitable for responding to drug
under trail (Laing et al. 2011). The identification of suitable volunteer individuals for
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drug trail can be performed by genetic tests which offer details about their genetic
variations, metabolic conditions, drug targets, polymorphisms in metabolic
enzymes, and existence of particular disorders. In this way limited number of
participants chosen will provide statistically significant outcomes, lowering the
total expenses and enhancing the efficacy of clinical trials. SNPs may help in the
development of tailored medication for a broad range of disorders (Laing et al.
2011). Pharmaco-genomics, therefore, primarily depends on SNPs for optimizing
the safety of drugs and effective treatment well appropriate for the specific genetic
profile of a particular patient, i.e., tailored medication or right medication for right
patient.
2.6 Utilizing SNP Maps in Pharmaco-Genomics
SNPs maps can be utilized in the field of pharmaco-genomics via two important
approaches (Jeanette and McCarthy 2000) the candidate gene approach and linkage
disequilibrium mapping.
2.6.1 Candidate Gene Approach
This approach utilizes paradigms of biology or previous understanding of pathogen-
esis of diseases to detect disease-related genes. SNPs present in such genes are
evaluated in patients registered for cohort, familial, or case-co ntrol studies for
clinical correlation with diseases. It is believed that such susceptibility genes specifi-
cally affect the probability of disease development in patients. This technique by
now has been utilized for the identification of candidate genes that affect the drug
reaction. The variants of drug-metabolizing enzyme gene such as thiopurine
methyltransferase (TPMT) have been associated with adverse drug responses
(Krynetski and Evans 1999). Similarly, variants of drug target gene such as
5-lipoxygenase (ALOX5) have been related with drug reaction variability (Drazen
et al. 1999) and gene variants of apolipoprotein E (APOE)-a disease susceptibility
gene have also been linked with cholinesterase inhibitor response in patients
suffering from Alzheimer’s disease (Poirier et al. 1995).
2.6.2 Linkage Disequilibrium Mapping
This approach is a substitute for candidate gene technique and depends on disequi-
librium linkage or non-random interaction between closely positioned SNPs. There
are thous ands of unknown SNPs in genome that need identification and mappi ng for
their position. Even though these unidentified SNPs may indeed be presen t as
susceptibility SNPs in genes but most of them are found between genes in the
huge non-coding regions of DNA and do not play an apparent part in drug reaction.
After finding the relationships via linkage disequilibrium (LD) these unknown
2 Single Nucleotide Polymorphisms and Pharmacogenomics 35
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biomarkers can help in identifi cation of a genomic region containing a susceptibility
gene having unknown identity and location. Subsequently, in order to understand the
fundamental relationship extra substantial efforts are needed using positional cloning
to identify the particular gene along with SNPs present in it. Monogenic disease
causing genes have been effectively diagnosed with the application of linkage
disequilibrium mapping in families having many affected individuals (Friedman
et al. 1995). Although currently different association studies use LD mapping
technique but it has failed to identify genetic predictors each of drug reaction or
disease in unrelated persons.
2.7 Pharmaco-Genomic Effects of SNPs
The increasing number of instances are available that correlate SNPs and other gene
polymorphisms with imperfect protein activity, possibility of over-toxicity, changes
in therapeutic advantages. Genetic variants of cytochrome P450 enzymes and
thiopurine methyl transferase present important instances for this kind of polymor-
phism. Currently several programs are available that predict effects of SNPs
(Table 2.3).
2.7.1 Pharmaco-Genomic Effects of Cytochrome P450 SNPs
Cytochrome P450 iso-enzymes (CYP450s) constitute a super-family of enzymes
consisting of haemoproteins. These enzymes are present on endoplasmic reticular
membrane and catalyze the metabolic process of a broad spectrum of internal
and external substances. CYP450s act as the key cause of variation in drug reaction
and pharmacokinetics (Danielson 2002). So far in human beings 57 functional genes
and 58 pseudo-genes have been documented as CYP super-family members
(Table 2.4). However, merely some enzymes of CYP1, CYP2, and CYP3 family
cause biotransformation of majority of exogenous substances including 70–80% of
clinically used drugs (Nelson 2013).
All CYPs exhibit variations in their expression that is influenced by numer ous
bio-factors and mechanisms such as gene polymorphisms, state of disease,
hormones, etc. (Zanger and Schwab 2013). SNPs are the main CYP polymorphisms.
SNPs of members of CYP genes (Table 2.5) introduce heterogeneity in their
expression level and eventu ally in activity that results in severe impact on drug
reaction and efficacy (Nebert et al. 2013). Gene polymorphisms are extremely
ethnically based and play a key part in the functioning of certain CYP representatives
including 2A6, 2B6, 2C9, 2D6, 2C19, and 3A5 as well as produce phenotypically
distinct metabolizers, viz. poor, intermediate, extensive, and ultra-rapid. CYP450s
play a significant role in the biotransformation of many drugs administered as
pro-drugs (inactive drug form) into their active form (Huttunen et al. 2008; Ortiz
de Montellano 2013). SNPs of CYP representative genes affect the biotransforma-
tion of pro-drugs. Therefore, CYP450 polymorphisms besides affecting the
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metabolism of drugs influence the pro-drug biotransformation as well as cause
variability in drug reaction against the similar drug. This potent attribute of
CYP450 is employed for the delivery of targeted drugs (Huttunen et al., 2008).
The information of families of CYP450 and their interactions with drugs can be
obtained from the available biological databases (Preissner et al. 2010).
2.7.2 Pharmaco-genomic effects of Thiopurine Methyltransferase
(TPMT) SNPs
The enzy me thiopurine S-methyltransferase metabolizes thiopurine drugs such as
azathioprine, 6-thioguanine, and 6-mercaptopurine, and other heterocyclic sulfhy-
dryl and aromatic compounds (Rutherford and Daggett 2008). These cytotoxic
compounds are commonly utilized for the treatment of inflammatory bowel diseases
Table 2.3 Various programs for the prediction of SNP effects
S
No Program/tool Description
1. SIFT A software for evaluating the effect of laboratory-induced
mutation (missense or non-synonymous) on the function of a
protein on the basis of amino-acid sequence homology and
physical properties
2. Mutation taster This tool helps in evaluating DNA gene variants to find their
propensity for disease. It helps in estimating the effect of variants
on gene product or protein via performing a series of silico tests
3. SuSPect Predicts human gene variants
4. Predict SNP Predicts human disease-related gene mutations
5. EnsemblVariant
effect predictor
This tool measures the impact of variants such as deletions, SNPs,
insertions, structural variants or copy number variations on genes,
transcripts, protein sequences, and regulatory regions
6. SNAP2 Annotates functional sequence variant effects
7. LIST This software determines the possible deleterious effects of gene
resulting from modifying their protein functions.
8. PolyPhen2 This tool predicts the potential effect of amino-acid substitution
on human protein structure and activity simply via relative and
physical aspects
9. SNPViz This software gives a 3-dimensional image of affected protein,
displays the modifications in amino acids that helps in
determining mutant protein’s pathogenecity
10. PROVEAN This program predicts the effect of amino-acid substitution and
indel on protein activity and filters sequence variations for the
identification of functionally vital indel or non-synonymous
variants
11. PhyreRisk This database that maps gene variants into experimental and
predicted protein structures
12. Missense3D Provide stereo-chemical information of the impact of missense
variations on the structure of protein
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and leukemia. With the discovery of TPMT gene polymorphisms (Table 2.6) and the
effects they exert on drug reaction, it has become an important topic of research
globally among medical researchers and biologists. Among the presently known
36 TPMT gene (http://www.imh.liu.se/) resulting in intermediate to no activity of
TPMT, the majority of which possess non-synonymous type of SNPs (Salavaggione
et al. 2005). Individuals carrying two homozygous non-functional variants of TPMT
have a higher risk of adverse side effects with thiopurine medication during their
care. Non-functional alleles are fairly uncommon and differ among ethnic groups in
their existence and frequency such as TPMT*3A allele is the most widespread in
Table 2.4 CYP families of humans and their role
CYP
family Alleles Role
CYP1 CYP1A1, CYP1A2, CYP1B1 Metabolism of drugs and steroids
particularly estrogen
CYP2 CYP2A6, CYP2A7,CYP2A13,
CYP2B6,CYP2C8, CYP2C9, CYP2C18,
CYP2C19,CYP2D6, CYP2E1, CYP2F1,
CYP2J2, CYP2R1, CYP2S1, CYP2U1,
CYP2W1
Metabolism of drugs and steroids
CYP3 CYP3A4, CYP3A5, CYP3A7,
CYP3A43
Metabolism of drugs and steroids
including hormone testosterone
CYP4 CYP4A11, CYP4A22, CYP4B1,
CYP4F2, CYP4F3, CYP4F8, CYP4F11,
CYP4F12, CYP4F22, CYP4V2,
CYP4X1, CYP4Z1
Metabolism of fatty acids or archidonic
acid
CYP5 CYP5A1 Synthesis of thromboxane A2
CYP7 CYP7A1, CYP7B1 Biosynthesis of bile acid via
7α-hydroxylation of cholesterol
CYP8 CYP8A1, CYP8B1 Prostacyclin synthesis (CYP8A1),
biosynthesis of bile acid (CYP8B1)
CYP11 CYP11A1, CYP11B1, CYP11B2 Biosynthesis of steroids
CYP17 CYP17A1 17α- hydroxylation, biosynthesis of
steroids
CYP19 CYP19A1 Biosynthesis of steroids
CYP20 CYP20A1 Function not known
CYP21 CYP21A2 Biosynthesis of steroids
CYP24 CYP24A1 Degradation of vitamin D
CYP26 CYP26A1, CYP26B1, CYP26C1 Hydroxylation of retinoic acid
CYP27 CYP27A1, CYP27B1, CYP27C1 Biosynthesis of bile acid (CYP27A1),
activation of vitamin D3 by
1α-hydroxylation of vitamin D3
(CYP27B1)
CYP39 CYP39A1 7α-hudroxylation of
24-hydroxycholestrol
CYP46 CYP46A1 Hydroxylation of cholesterol
CYP51 CYP51A1 Biosynthesis of cholesterol
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Table 2.5 Selected Cytochrome-P450 gene polymorphisms with known effects in different populations
Gene Alleles Location SNPs Affected populations Activity and effects
CYP1A2 CYP1A2*1 15q24.1 Wild type General Codes for 515-residue protein
CYP1A2*1C Enchancer G3860A Japanese Linked with decreased 1A2 inducibility
CYP1A2*1F Intron1 C163A British-whites (33%), German-
whites (32%)
Linked with increased activity or
formation of 1A2.
CYP1A2*1 K Intron1 164A, 740G,
730 T
Saudi Arabians (3.6%), Spaniards
(0.5%)
Interrupts binding of transcriptional factor
leading to decreased transcription
CYP1A2*3 Exon 4 T5347T French (0.5–1%) Down-regulates gene expression
CYP1A2*4 Exon 5 A2499T French (0.5–1%) Down-regulates gene expression
CYP1A2*6 Exon7 G5090T French (0.5–1%) Down-regulates gene expression
CYP1A2*7 Intron6 G3533A – Gene splicing defects
CYP3A4 CYP3A4*1B 5
0
-promoter A392G African-Americans (53%),
Caucasians (9%), Asian (0%)
Missense mutations leading to decreased
enzyme production, reduced activity
CYP3A4*2 Exon 7 T15713C African-Americans (0%), Asian
(0%), Caucasians (2.7%)
Missense mutations, reduced activity
CYP3A4*3 Exon 12 T23171C Caucasians (1.1%) Missense mutations, reduced activity
CYP3A4*6 Exon 9 17661_176622insA
(227Framshift)
Asian <1% Causes early stop codon, activity reduced
CYP3A4*11 Exon 11 C21867T (T363M) German or Swis Caucasian <1% Decreased gene expression and as a result
decreased activity
CYP3A4*15A Exon 6 G14269A (R162Q) African-Americans (2–4%) Missense mutation and reduced activity
CYP3A4*16B Exon
7, intron 10
C15603G (T185S) Japanese (1.4%) Missense mutation and reduced activity
CYP3A4*20 – 25889_25890insA
(488Framshift)
German-Caucasian (<0.06%) Insertion leads to premature stop codon
where no CYP3A4 enzyme is formed or
functional, reduced function
CYP3A4*21 Exon 10 A956G (Y319C) Han-Chinese (0.5%) May induce protein conformation
(continued)
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Table 2.5 (continued)
Gene Alleles Location SNPs Affected populations Activity and effects
CYP2C9 CYP2C9*1 10q24.1 Wild type General Normal activity
CYP2C9*2 Exon 3 RI44C (3608C ! T) African-American (1.3%),
European decent (12.8%)
Decreased activity
CYP2C9*3 Exon 7 I359L (42614C ! G) African-American (1.9%), Asians
(2–4%), European decent (6.3%)
Decreased activity
CYP2C9*5 Exon 7 D360E (42619C ! G) African-Americans (0.9–1.8%) Decreased activity
CYP2C9*6 Exon 5 10601delA (818delA) African-Americans (0.1–0.75%) Null
CYP2C9*11 Exon 7 R335W (1003C ! T) African-Americans (1.5%) Decreased activity
CYP2C19 CYP2C19*1 10q24.1–3 Wild type General Extensive metabolizers, activity normal
CYP2C19*2 Exon 5 40 bp deletion
(681G ! A)
Japanese (18–23%), Caucasians
(2–5%)
Poor metabolizers, decreased activity
CYP2C19*3 Exon 4 W212X (636G ! A) Vanuatu islands (13%) Premature stop codon, poor metabolizers,
decreased activity
CYP2C19*4 Initiation
codon
M1V (mutation in
initiation codon A ! G
Caucasians (0.6–3%) Defective initiation codon, poor
metabolizers, decreased activity
CYP2C19*5 Exon 9 R433W (1297 C ! T) Chinese (low), Japanese (low) Poor metabolizers, decreased activity
CYP2C19*17 5
0
-flanking
region
C806T (3402C ! T) 18% Ethiopians and swedes,
1.3% Japanese, 0.64% Chinese
Elevated transcription of 2C19 gene,
ultra-rapid metabolizers
CYP2D6 CYP2D6*1 22Q13.1 Wild type General Extensive metabolizers, activity normal
CYP2D6*2xn Exon 6 and
9
Duplication of genes
C2983T (R296C),
G4268C (S486T)
Caucasians (1–5%), Asians
(0–2%), Ethiopians/ Saudi
Arabians (10–16%)
Extensive or ultra-rapid metabolizers,
increased activity
CYP2D6*3 Exon 5 1 bp deletion 2637A/ aka.
2549A)
Asians (0%) Poor metabolizers as a result of frame shift
mutation,
CYP2D6*4 Exon 3 and
4 junction
G1934A African-Americans (2%) Poor metabolizers or showing no
metabolic action as a result of splicing
defect
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CYP2D6*6 Exon 3 T1795 deletion leading to
premature stop codon
Caucasians (1.8%) Poor metabolizers, non-functioning
variants
CYP2D6*10 Exon 1 C188T/C100T(P34S) African-Americans (6%),
Caucasians (1–2%), Asians
(39–52%)
Intermediate metabolizers, decreased
activity
CYP2D6*14 Exon 3 P34S, R296C, S486T,
G169R
Asian (2.2%) Enzymatic non-functionality, poor
metabolizers
CYP2D6*17 Intron 1 C111T (T107I), G4268C
(S486T), C2938T
(R2968)
African-Americans (20–35%) Affinity change for 2D6 substrates,
decreased activity
CYP2D6*29 – G3183A Black Africans(20%) Poor metabolizers, decreased activity
CYP2D6*41 Intron 6 G2988A African-Americans (11%),
Caucasians (8–10%), Japanese
(2–6%)
Intermediate metabolizers as a result of
abnormal splicing, decreased activity
2 Single Nucleotide Polymorphisms and Pharmacogenomics 41
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Caucasian population with nearly 5% frequency. Similarly, TPMT*3C allele most
commonly exists in the populations of East-Asian and African-American
populations. Likewise, TPMT*8 allele is more commonly present in the population
Africa (http://www.pharmgkb.org). TPMT served as a model for numerous genetic
polymorphism-based clinical trials since the advent of perception of pharmaco-
genomics and today it presents an excellent instance of successful translation of
pharmaco-genomics into regular medical practice (Appell et al. 2013).
2.7.3 Pharmaco-Genomic Effects of N-Acetyl Transferase
2 (NAT2) SNPs
The gene NAT2 is a key enzyme that catalyzes N-acetylation of sulfasalazine. It is
positioned on short arm of chromosome 8 (8p22). NAT2 gene polymorphisms
influence the acetylation rate as a result of which the efficiency and toxicity of the
drug sulfasalazine is modified (Ladero 2008). This drug is normally employed for
treating rheumatic arthritis and it works by inhibiting the function of neutrophils,
decreasing the levels of immunoglobulin as well as interferes with the functions of T
lymphocytes via suppressing necrosis factor -jB (Gadangi et al. 1996). The patients
that suffer from rheumatic arthritis and possess the variants of NAT2 gene including
NAT2*5A, NAT2*5B, NAT2*5C, NAT2*6, and NAT2*7 are more susceptible to
adverse drug reaction compared to wild type haplotype (NAT2*4) (Taniguchi et al.
2007; Ladero 2008). With the help of genomic data available related to NAT2 gene
the severity of rheumatic arthritis patients will be classified as highly severe (homo-
zygous NAT2*4), moderately severe (heterozygous NAT2*4 along with its allele
variants), and rarely severe (homozygous allele variant) (Kumagai et al. 2004).
Table 2.6 Selected SNPs in TMPT gene
TMPT gene
variants SNPs Position Activity References
TMPT*2 238G > C Exon 5 Decreased Do and Dobrovic (2015)
TMPT*3A 460G > G,
719A > G
Exon
7, exon 10
Decreased Thatcher (2015)
TMPT*3B 460G > A Exon 7 Decreased Marrugo-Ramirez et al. (2018),
Hofman (2019)
TMPT*4 IVS91G > A Intron
9/exon 10
Decreased Dagogo-Jack and Shaw (2018)
TMPT*5 146 T > C Exon 4 Decreased Dundas et al. (2008)
TMPT*6 539A > T Exon 8 Decreased Dundas et al. (2008)
TMPT*7 681 T > G Exon 10 Decreased Riemann et al. (2007)
TMPT*8 644G > A Exon 10 Intermediate Chomczynski and Sacchi
(2006)
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