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People vary in their response to the same medicine. Few medicines are effective for everyone; all may cause adverse reactions or occasionally death. Some of the variation between individuals in response to medicines is due to differences in their genetic make-up (Lazarou et al., 1998). There are many different reasons why medicines may be dangerous or ineffective, such as inaccurate prescribing, poor compliance by the patient and interaction between a particular medicine and other substances, including other medication. However, advances in genetic knowledge may enable us to take better account of differences between individuals (Pistoi, 2002).

Pharmacogenetics is the study of genetic variation that affects response to medicines. It has the potential to play an important role in improving safety and efficacy. Adverse reactions to medicines have significant costs, in both human and monetary terms. In addition, considerable resources are wasted on prescribing medicines that have little or no effect in particular patients (Niazi and Riaz-ud-Din, 2006).

The option of using genetic information to predict response to medicines has led some to make the optimistic claim that the development of ‘personalized’ medicine, or ‘the right medicine for the right patient, at the right dose’, is only a matter of time. Such claims require careful assessment. Pharmacogenetics does have the potential to improve the quality of patient care significantly. Just how quickly and effectively this technology can be deployed is unclear. There are few current applications of pharmacogenetic testing, and we do not know to what degree possible application of pharmacogenetics can be realized in practice. Several different factors will influence the proportion of patients who will come to benefit from pharmacogenetics, not least the constraints imposed by the complexity of human response to medicines (Couteur et al., 2002; Shenfield et al., 2002).

Pharmacogenetics is the branch of pharmacology, which deals with the influence of genetic variation on drug response in patients by correlating gene expression or single-nucleotide polymorphisms with a drug’s efficacy or toxicity. By doing so, Pharmacogenetics aims to develop rational means to optimize drug therapy, with respect to the patients’ genotype, to ensure maximum efficacy with minimal adverse effects. Such approaches promise the advent of “personalized medicine”; in which drugs and drug combinations are optimized for each individual’s unique genetic makeup (Redei, 2003).


There are a number of factors that affect our response to medicines, including age, sex, interaction with other medicines, and diet. A genetic variant alone will rarely provide a precise prediction of the response of a particular patient.

Differences in our genes can affect the way in which we respond to medicines in two ways:

Variation in the way the body processes a medicine (Differentiating people)

Variation in DNA can lead to altered activity of enzymes that are responsible for absorption, metabolism and excretion of medicines. If a medicine is broken down too quickly it may not be effective. Alternatively, slow metabolism can lead to the build-up of toxic levels of a medicine. Even a single change in the DNA can affect the way the body processes a medicine.

An enzyme in the liver, CYP2D6, is involved in the metabolism of nearly one quarter of medicines, including anti-depressants and beta-blockers used to treat heart disease. Variations in the CYP2D6 gene may alter the activity of this enzyme. People with reduced levels are unable to process some medicines properly. In some cases the medicine will not be effective; in other cases patients will suffer serious side effects. For example, the painkiller codeine has no effect on approximately 7% of Caucasians because of a CYP2D6 variant. Variation in the genetic characteristics of a disease (Differentiating disease)

Many diseases that are currently diagnosed as a single clinical condition may actually have a number of underlying causes, based on different genetic characteristics. Understanding more about the genetic basis of disease may provide information about what medicine would be effective. Some cells, for example those in cancerous tumors, have an altered genetic make-up. This change only occurs in the cancer cells, and not in normal tissue, and may influence treatments.

A particularly aggressive form of breast cancer is associated with a genetic variation, which leads to overproduction of a protein called HER2. Patients with breast cancer can be tested to find out if they have high levels of HER2. If they do, the medicine Herceptin (trastuzumab) is given to treat this specific type of cancer.

Fig 1.1: Genetic response of the individuals to the drug


Pharmacogenetics, the study of hereditary variations in drug response, is a term that first appeared in the literature in the mid-1950s. Advances in genetics, including the sequencing of the human genome, have led to a better understanding of how hereditary variation in drug metabolism, transport and response can be applied to individualized patient care through diagnostic testing (Noah, 2002).

Pharmacogenetics has been around in some form since the 1930s. In 1902, Archibald Garrod first asserted the hypothesis that genetic variations could cause adverse biological reactions when chemical substances were ingested. He also suggested that enzymes were responsible for detoxifying foreign substances, and that some people do not have the ability to eliminate certain foreign substances from the body because they lack enzymes required to break down these materials (Horton et al., 2004).

The first pharmacogenetic study took place in 1932, when the inability to taste a chemical compound known as phenylthiocarbamide was linked to an autosomal recessive trait. An autosome is a chromosome that does not participate in sex determination, and therefore refers to all the cells in the body except for sperm and eggs. Recessive traits are described as follows: each person has two genes that code for a particular trait — one is inherited from the mother and one is inherited from the father. If a person inherits two different alternative forms of a gene, called alleles, the trait that is expressed physically as a phenotype is the dominant trait, while the one not expressed is a recessive trait. Examples of recessive traits include hitchhikers thumb and blue eyes (Brown, 2003).

In the 1940s and 1950s, scientists first began to note “variable drug responses” in people taking various preventive medications. Drug reactions based on inherited traits were first recorded during World War II, when some soldiers developed anemia after receiving doses of the anti-malarial drug primaquine (Carson et al., 1956). Later studies confirmed that the anemia was caused by a genetic deficiency of the glucose-6-phosphate dehydrogenase enzyme (Al-Ali, 2009). Similar reactions to succinlcholine and isoniazid were studied, and revealed that deficiencies in enzymes led to an inability to metabolize those drugs. After studying adverse drug reactions to primaquine, succinlcholine, and isoniazid, Arco Moltulsky proposed in 1957 that inherited traits may not only lead to adverse drug reactions, but may also affect whether the drugs actually work (Veenstra, 2004).

In recent decades, further progress has been made in isolating genetic variations in major drug-metabolizing enzymes, including cytochrome P450. Scientists first began to study cytochrome P450 when some patients experienced a severe decline in blood pressure while taking debrisoquin, an anti-hypertensive drug (Silber et al., 2001). The study revealed that these patients had two recessive alleles for the enzyme, resulting in an inability to metabolize the drug. Approximately ten percent of the population metabolizes cytochrome P450 poorly, experiencing adverse effects and reduced drug uptake when they take drugs in the family of chemicals metabolized by the enzyme. The evaluation of cytochrome P450 has led to the identification and characterization of many other drug-metabolizing enzymes (Miller & Levine, 2000).

The human genome sequence enabled comparisons of genetic sequences between individuals and led to the discovery that the nucleotide sequence for many if not all-human genes exhibits a natural rate of variability. This characteristic is referred to as genetic polymorphism. Genetic polymorphism can occur in the form of single nucleotide substitutions, insertions, or deletions (Milan et al., 2002). Short stretches of repetitive nucleotide sequences, complete gene deletion and gene duplications are the basis for our individual physical characteristics, including the way we respond to specific medications.


1. More Powerful Medicines

Pharmaceutical companies will be able to create drugs based on the proteins, enzymes, and RNA molecules associated with genes and diseases. This will facilitate drug discovery and allow drug makers to produce a therapy more targeted to specific diseases. This accuracy not only will maximize therapeutic effects but also decrease damage to nearby healthy cells (Redei, 2003).

2. Better, Safer Drugs for the First Time

Instead of the standard trial-and-error method of matching patients with the right drugs, doctors will be able to analyze a patient’s genetic profile and prescribe the best available drug therapy from the beginning. Not only will this take the guesswork out of finding the right drug, it will speed recovery time and increase safety as the likelihood of adverse reactions is eliminated. Pharmacogenetics has the potential to dramatically reduce the estimated 100,000 deaths and 2 million hospitalizations that occur each year in the United States as the result of adverse drug response (Rothstein, 2003).

3. More Accurate Methods of Determining Appropriate Drug Dosages

Current methods of basing dosages on weight and age will be replaced with dosages based on a person’s genetics i.e. how well the body processes the medicine and the time it takes to metabolize it. This will maximize the therapy’s value and decrease the likelihood of overdose.

4. Advanced Screening for Disease

Knowing one’s genetic code will allow a person to make adequate lifestyle and environmental changes at an early age so as to avoid or lessen the severity of a genetic disease. Likewise, advance knowledge of particular disease susceptibility will allow careful monitoring, and treatments can be introduced at the most appropriate stage to maximize their therapy (Licinio and Wong, 2002).

5. Better Vaccines

Vaccines made of genetic material, either DNA or RNA; promise all the benefits of existing vaccines without all the risks. They will activate the immune system but will be unable to cause infections. They will be inexpensive, stable, easy to store, and capable of being engineered to carry several strains of a pathogen at once (Gage et al., 2008; Eby et al., 2009).

6. Improvements in the Drug Discovery and Approval Process

Pharmaceutical companies will be able to discover potential therapies more easily using genome targets. Previously failed drug candidates may be revived as they are matched with the niche population they serve. The drug approval process should be facilitated as trials are targeted for specific genetic population groups –providing greater degrees of success. The cost and risk of clinical trials will be reduced by targeting only those persons capable of responding to a drug (Milligan, 2009; Banet et al., 2009).

7. Decrease in the Overall Cost of Health Care

Decreases in the number of adverse drug reactions, the number of failed drug trials, the time it takes to get a drug approved, the length of time patients are on medication, the number of medications patients must take to find an effective therapy, the effects of a disease on the body (through early detection), and an increase in the range of possible drug targets will promote a net decrease in the cost of health care (Duncan et al., 2007).

8. Enhancing efficacy

Many medicines are not effective for everyone with a particular disease. Some common treatments for diabetes, depression and asthma are only effective in around 60% of patients. Pharmacogenetics could allow doctors to prescribe medicine only for those patients most likely to respond. Alternatively, new medicines could be designed on the basis of genetic information about the cause of disease (McLeod et al., 2009).


A genetic polymorphism is defined as a deoxyribonucleic acid (DNA) sequence variant which is stable within a population and occurs with a frequency equal to or greater than 1% (Nebert, 1999; Bachmann, 2002; Lash et al., 2003). For example, a genetic polymorphism might give rise to blue eyes versus brown eyes, or straight hair versus curly hair. Genetic polymorphism may be the result of chance processes, or may have been induced by external agents (such as viruses or radiation). If a difference in DNA sequence among individuals has been shown to be associated with disease, it will usually be called a genetic mutation. Changes in DNA sequence that have been confirmed to be caused by external agents are also generally called mutations rather than polymorphisms (Afna et al., 2001; Lancia et al., 2001). There is a considerable level of variability between individuals at the genetic level, as manifested by the polymorphisms present within their genome (Sachidanandam et al., 2001; Oscarson, 2003).

Over 90% of these polymorphisms are believed to be accounted for by changes in a single nucleotide, namely Single nucleotide polymorphisms (SNPs), with the remainder of the variation caused by insertions and deletions (indels), variable number tandem repeats (VNTRs) and microsatellites (Quirk et al., 2004; Marsh and McLeod, 2006). However, unlike many other previously characterized polymorphisms, such as VNTRs and microsatellites, SNPs are often found within the coding and regulatory regions of genes and thus can have functional consequences for gene expression and gene product functionality (Campbell et al., 2000; Gray et al., 2000).


A single nucleotide polymorphism is a source variance in a genome. A SNP (snip) is a single base mutation in DNA. SNPs are the most simple form and most common source of genetic polymorphism in the human genome (90% of all human DNA polymorphism) (Lippert et al., 2002).

There are two types of nucleotide base substitutions resulting in SNPs:

A transition substitution occurs between purines (A, G) or between Pyrimidines (C, T). This type of substitution constitutes two third of all SNPs (Schwartz et al., 2002)

A transversion substitution occurs between a purine and a pyrimidine.

1.2.2 STRs, VNTRs

Differences in single base pairs, known as single nucleotide polymorphisms (SNPs), are a valuable class of polymorphism that can be detected by DNA sequencing, RFLP analysis, and other methods such as allele-specific PCR and allele-specific DNA hybridization. These sequences consist of several repeats of a simple DNA sequence pattern, and they typically do not encode a protein or have strict requirements of size and sequence. For example, the two base pairs cytosine (C) and adenine (A) may be found together multiple times, resulting in a “CACACACA” sequence. If another copy of this sequence were found as “CACA” (two CA pairs shorter), then this sequence would be polymorphic. Repetitive genetic elements include microsatellites or STRs (short tandem repeats) and the minisatellites or VNTRs (variable number of tandem repeats), which are distinguished primarily on the basis of size and repeat pattern: The repeated sequence in microsatellites range from two to six bases, while in a VNTR it ranges from eleven to sixty base pairs (Eschenhagen et al., 2003).


Sequence variation caused by SNPs can be measured in terms of nucleotide diversity, the ration of the number of base differences between two genomes over the number of bases compared. This is approximately 1/1000 (1/1350) base pairs between two equivalent chromosomes. (Lancia et al., 2002)


SNPs are not uniformly distributed over the entire human genome, neither over all chromosomes and neither within a single chromosome. There are one third as many SNPs within coding regions as non-coding region SNPs. It has also been shown that sequence variation is much lower for the sex chromosomes. Within a single chromosome, SNPs can be concentrated about a specific region, usually implying a region of medical or research interest. For instance, the sequence that encodes proteins that present antigens to the immune system in chromosome 6 displays very high nucleotide diversity compared to the other areas of that chromosome (Istrail et al., 2002)


A SNP in a coding region may have two different effects on the resulting protein:

Synonymous the substitution caused no amino acid change to the protein it produces. This is also called a silent mutation.

Non-Synonymous the substitution results in an alteration of the encoded amino acid. A missense mutation changes the protein by causing a change of codon. A nonsense mutation results in a misplaced termination codon. One half of all coding sequence SNPs results in non-synonymous codon changes.

SNPs may occur in regulatory regions of genes. These SNPs are capable of changing the amount of timing of a proteins production. Such SNPs are much more difficult of find and understand and gene regulation itself is not yet clearly understood (Jin et al., 2002).


There are over one million SNPs identified (1,255,326 mapped SNPs at the SNP Consortium Organization). Validation experiments have shown that 95% of these are unique and valid polymorphisms (not the product of error or redundancy).

Methods for SNP discovery/detection involve a set of biochemical reactions that isolates the precise location of a suspected SNP and then directly determines the identity of the SNP, using an enzyme called DNA polymerase (Afna et al., 2001; Lancia, 2001).

Also many SNPs were initially detected by comparing different sequenced genomes. This work has now been extended to a much larger-scale effort to determine the SNPs (genotypes) of many genomes from different populations.

Notice the difference between SNP discovery/detection and SNP scoring or SNP genotyping. One strives to identify new SNP location on the genome, while the other involves methods to determine the genotypes of many individuals for particular SNPs that have already been discovered (Sacher et al., 2010). RESTRICTION FRAGMENT LENGTH POLYMORPHISMS (RFLPS)

An early method of detecting DNA polymorphisms still in use employs restriction endonucleases. These bacterial enzymes cut DNA at specific recognition sequences. Restriction enzymes cleave DNA into a characteristic set of fragments that can be separated by gel electrophoresis. Some polymorphisms alter recognition sequences, so that the enzyme no longer recognizes a site or recognizes a new site. This results in a new set of DNA fragments that can be compared to others to detect the differences. These differences are called restriction fragment length polymorphisms (RFLPs) (Saiki et al., 1985, Osborn et al., 2000). HIGH-THROUGHPUT SNP GENOTYPING

The second phase of human genomics (the first being the sequencing of the human genome) involves large scale screening of different human populations for significant DNA polymorphisms. The information gathered will lead to accurate association between genotype and phenotype.

High-throughput SNP genotyping is the process of quickly and cost-effectively identifying the SNP values in as many different individual human genomes as possible. Steps of SNP genotyping include DNA sample preparation, PCR amplification, and microarray assays. For the last step, the technology must label SNP location of both alleles in the DNA sample and determine the base values using microarray technology (Dracopoli et al., 2002).

Orchid Biocomputer and Affymetrix are the leaders in providing SNP genotyping technology. They have developed single nucleotide polymorphism (SNPs) genotyping assays that combine Orchids proprietary GBA primer extension technology with an Affymetrix GeneChip Universal array. Their technologies can provide ultra-high throughput of 100,000 genotypes a day. It is interesting to note that nearly all SNP genotyping uses Affymetrix equipment (including GenFlex, Affymetrix universal microarray).


The study of polymorphism has many uses in medicine, biological research, and law enforcement. Genetic diseases may be caused by a specific polymorphism. Scientists can look for these polymorphisms to determine if a person will develop the disease, or risks passing it on to his or her children. Besides being useful in identifying people at risk for a genetically based disease, knowledge of polymorphisms that cause disease can provide valuable insight into how the disease develops (Smith et al., 1998). Polymorphisms located near a disease gene can be used to find the gene itself, through mapping. In this process, researchers look for polymorphisms that are co-inherited with the disease. By finding linked polymorphisms on smaller and smaller regions of the chromosome, the chromosome region implicated in the disease can be progressively narrowed, and the responsible gene ultimately can be located (Harris et al., 1980)

A related use of polymorphism is widely employed in agriculture. If a polymorphism can be identified that is associated with a desirable characteristic in an agriculturally important plant or animal, then this polymorphism can be used as a genetic flag to identify individuals that have the desirable characteristic (Ho and Clegg, 1998). Using this technique, known as marker-assisted selection, breeding programs aimed at improving agriculturally important plants and animals can be made more efficient, since individuals that have the desired trait can be identified before the trait becomes apparent (Thein et al., 1998; Kidd, 2001).

Polymorphisms can be used to illuminate fundamental biological patterns and processes. By studying polymorphisms in a group of wild animals, the familial relationships (brother, sister, mother, father, etc.) between them can be determined (Hopkinson et al., 1999). The amount of interbreeding between different groups of the same species (gene flow) can be estimated by studying the polymorphisms they contain. This information can be used to identify unique populations that may be important for survival of the species. Sometimes it is not immediately obvious if two different groups of organisms should be classified as different species (Whitehouse et al., 1999).


1.3.1 Genotype

The genotype is the genetic constitution of a cell, an organism, or an individual (i.e. the specific allele makeup of the individual) usually with reference to a specific character under consideration. Genotyping is the process of elucidating the genotype of an individual with a biological assay (Karger et al., 2007; Gillies et al., 2008). Also known as a genotypic assay, techniques include PCR, DNA fragment analysis, allele specific oligonucleotide (ASO) probes, DNA sequencing, and nucleic acid hybridization to DNA microarrays or beads. Several common genotyping techniques include restriction fragment length polymorphism (RFLP); terminal restriction fragment length polymorphism (t-RFLP), amplified fragment length polymorphism (AFLP), and multiplex ligation-dependent probe amplification (MLPA).

For example, hemophilia. Due to the diploidy of humans (and most animals), there are two alleles for any given gene (Gerstein et al., 2007). These alleles can be the same (homozygous) or different (heterozygous), depending on the individual. With a dominant allele, the offspring is guaranteed to inherit the trait in question irrespective of the second allele.

1.3.2 Phenotype

A phenotype is any observable characteristic or trait of an organism: such as its morphology, development, biochemical or physiological properties, or behavior. Phenotypes result from the expression of an organism’s genes as well as the influence of environmental factors and possible interactions between the two (Fluri, 2000; Rivier et al., 2001).

The genotype of an organism is the inherited instructions it carries within its genetic code. Not all organisms with the same genotype look or act the same way because appearance and behavior are modified by environmental and developmental conditions. Similarly, not all organisms that look alike necessarily have the same genotype (You et al., 2001).

1.3.3 Phenotypic variation

Phenotypic variation (due to underlying heritable genetic variation) is a fundamental prerequisite for evolution by natural selection. It is the living organism as a whole that contributes (or not) to the next generation, so natural selection affects the genetic structure of a population indirectly via the contribution of phenotypes. Without phenotypic variation, there would be no evolution by natural selection (Schweizer et al., 2001).

The interaction between genotype and phenotype has often been conceptualized by the following relationship:

Genotype + environment ? phenotype

A slightly more nuanced version of the relationships is:

Genotype + environment + random-variation ? phenotype

Genotypes often have much flexibility in the modification and expression of phenotypes; in many organisms these phenotypes are very different under varying environmental conditions. The plant Hieracium umbellatum is found growing in two different habitats in Sweden (Mangin et al., 2001; Saugy, 2001). One habitat is rocky, sea-side cliffs, where the plants are bushy with broad leaves and expanded inflorescences; the other is among sand dunes where the plants grow prostrate with narrow leaves and compact inflorescences. These habitats alternate along the coast of Sweden and the habitat that the seeds of Hieracium umbellatum land in, determine the phenotype that grows.

1.3.4 The Genotype/Phenotype Distinction

The distinction between phenotype and genotype is fundamental to the understanding of heredity and development of organisms (Arkin et al., 1997; Lewontin et al., 2004). The genotype of an organism is the class to which that organism belongs as determined by the description of the actual physical material made up of DNA that was passed to the organism by its parents at the organism’s conception. For sexually reproducing organisms that physical material consists of the DNA contributed to the fertilized egg by the sperm and egg of its two parents (Schmalhausen et al., 1949; Rendel et al., 1967). For asexually reproducing organisms, for example bacteria, the inherited material is a direct copy of the DNA of its parent. The phenotype of an organism is the class to which that organism belongs as determined by the description of the physical and behavioral characteristics of the organism, for example its size and shape, its metabolic activities and its pattern of movement.

Relationship between the genotype and phenotype

Genotype codes for the Phenotype. The “internally coded, inheritable information”, or Genotype, carried by all living organisms, holds the critical instructions that are used and interpreted by the cellular machinery of the cells to produce the “outward, physical manifestation”, or Phenotype of the organism (Blamire, 2000).

Fig 1.2: DNA transcription and translation

Gene encoded different proteins through transcription and translation, and thus responsible for particular activities or different physical characteristics. The amino acid sequence of proteins is directed by the information found in genes, which in turn are made up of DNA. Genes that have different DNA sequences are said to be polymorphic. These different gene forms are called alleles, exemplified by the alleles that control eye color. When alleles result in differences in the amino acid sequence of a protein, the proteins encoded by alleles are called isoforms. The position of the gene on a chromosome is its locus. More generally, a locus refers to any position on a chromosome, whether or not a gene is located there.

Genotype and phenotype represent very real differences between genetic composition and expressed form (Murfet et al., 1975; Keeton et al., 1986). The genotype is a group of genetic markers that describes the particular forms or variations of genes (alleles) carried by an individual. Accordingly, an individual’s genotype includes all the alleles carried by that individual. An individual’s genotype, because it includes all of the various alleles carried, determines the range of traits possible (e.g., a individual’s potential to be afflicted with a particular disease). In contrast to the possibilities contained within the genotype, the phenotype reflects the manifest expression of those possibilities (Beurton et al., 2000, Gilbert et al., 2000, Jorde et al., 2000, Lodish et al., 2000). Phenotypic traits include obvious observable traits as height, weight, eye color, hair color, etc. The presence or absence of a disease, or symptoms related to a particular disease state, is also a phenotypic trait.

A clear example of the relationship between genotype and phenotype exists in cases where there are dominant and recessive alleles for a particular trait (Hartwell et al., 2000, Lewis et al., 2001). Using an simplified monogenetic (one gene, one trait) example, a capital T might be used to represent a dominant allele at a particular locus coding for tallness in a particular plant, and the lowercase t used to represent the recessive allele coding for shorter plants. Using this notation, a diploid plant will possess one of three genotypes: TT, Tt, or tt (the variation tT is identical to Tt). Although there are three different genotypes, because of the laws governing dominance, the plants will be either tall or short (two phenotypes). Those plants with a TT or Tt genotype are observed to be tall (phenotypically tall). Only those plants that carry the tt genotype will be observed to be short (phenotypically short).

1.4 Drug Metabolism

The majority of drugs undergo a variety of chemical reactions in the liver and, to a much lesser extent, in other organs (e.g., intestinal wall, kidney, lungs). Such reactions include oxidation, reduction, hydrolysis, and conjugation (with glucuronic acid, amino acids, acetate, sulphate, and methyl groups) and are directed towards the production of metabolites that are more ionized, more water-soluble, and less capable of penetrating cell membranes and being sequestrated in tissues. The more polar or water-soluble a compound becomes, the more readily it is excreted through the kidney and hepato-biliary system (Cheng et al., 2009).

Drug metabolism is the metabolism of drugs, their biochemical modification or degradation, usually through specialized enzymatic systems. This is a form of xenobiotic metabolism. Drug metabolism often converts lipophilic chemical compounds into more readily excreted polar products. Its rate is an important determinant of the duration and intensity of the pharmacological action of drugs (Nelson et al., 1995, Welch et al., 2005)

Drug metabolism can result in toxication or detoxication – the activation or deactivation of the chemical. While both occur, the major metabolites of most drugs are detoxication products (Guo et al., 2004).


Phase I reactions usually precede Phase II, though not necessarily. During these reactions, polar bodies are either introduced or unmasked, which results in (more) polar metabolites of the original chemicals. In the case of pharmaceutical drugs, Phase I reactions can lead either to activation or inactivation of the drug (Smith et al., Stein et al., 2009).

Phase I reactions (also termed nonsynthetic reactions) may occur by oxidation, reduction, hydrolysis, cyclization, and decyclization reactions. Oxidation involves the enzymatic addition of oxygen or removal of hydrogen, carried out by mixed function oxidases, often in the liver (Hodjegan et al., 2007; Tucker, 2007). These oxidative reactions typically involve a cytochrome P450 monooxygenase (often abbreviated CYP), NADPH and oxygen. The classes of pharmaceutical drugs that utilize this method for their metabolism include phenothiazines, paracetamol, and steroids (Smith et al., 2009). If the metabolites of phase I reactions are sufficiently polar, they may be readily excreted at this point. However, many phase I products are not eliminated rapidly and undergo a subsequent reaction in which an endogenous substrate combines with the newly incorporated functional group to form a highly polar conjugate.

Phase II reactions — usually known as conjugation reactions (e.g., with glucuronic acid, sulfonates (commonly known as sulfation), glutathione or amino acids) — are usually detoxication in nature, and involve the interactions of the polar functional groups of phase I metabolites (Figadere et al., 2008). Sites on drugs where conjugation reactions occur include carboxyl (-COOH), hydroxyl (-OH), amino (NH2), and sulfhydryl (-SH) groups. Products of conjugation reactions have increased molecular weight and are usually inactive unlike Phase I reactions which often produce active metabolites. (Akagah et. al., 2008; Fournet et al., 2008).

The CYP-450 (pronounced “sip”) enzyme system is a well-known group of human enzymes that metabolize drugs and chemicals in the body. CYP-450 enzymes are mostly in the intestines and liver (Lormier et al., 2008).

Each CYP has a different ability to metabolize a given chemical or drug. For example, CYP3A4 is probably the most important drug metabolizing enzyme because it metabolizes the most drugs, including protease inhibitors (Hodjegan et al. 2007; Tucker et al., 2007).

1.4.2 Drug-metabolizing enzymes (DME)

Drug-metabolizing enzymes are called mixed-function oxidase or monooxygenase and containing many enzymes including cytochrome P450, cytochrome b5, and NADPH-cytochrome P450 reductase and other components (Sheweita, 2000). The hepatic cytochrome P450s (Cyp) are a multigene family of enzymes that play a critical role in the metabolism of many drugs and xenobiotics with each cytochrome isozyme responding differently to exogenous chemicals in terms of its induction and inhibition.

For example, CYP1A1 is particularly active towards polycyclic aromatic hydrocarbons (PAHs), activating them into reactive intermediates those covalently bind to DNA, a key event in the initiation of carcinogenesis (Engelbrecht et al., 2010; Sacher et al., 2010). Likewise, CYP1A2 activates a variety of bladder carcinogens, such as aromatic amines and amides. Also, some forms of cytochrome P450 isozymes such as CYP3A and 2E1 activate the naturally occurring carcinogens (e.g. aflatoxin B1) and N-nitrosamines respectively into highly mutagenic and carcinogenic agents (Jiang et al., 2001; Gonzalez et al., 2002).

1.4.3 THE effects of POLYMORPHISM OF DME (drug metabolizing enzyme)

A relatively small number of drug-metabolizing enzymes (DMEs) are responsible for metabolizing the majority of drug therapies in clinical use today. There are a relatively small number of relevant polymorphisms within these enzymes, and many of them can result in lack of therapeutic effect or in exacerbated clinical response (Arranz et al., 1998; Munro et al., 1998). Standard doses of drugs with a steep dose-response curve or a narrow therapeutic range may produce adverse drug reactions, toxicity, or decreased efficacy in PMs. When taken by UMs, the standard dose may be inadequate to produce the desired effect.

Two examples of polymorphisms in drug-metabolizing enzymes that have considerable potential to affect clinical medicine include those affecting the cytochrome P450 enzyme family, CYP2D6, and the enzyme thiopurine methyltransferase (TPMT). These genetic variations affect a significant percentage of the population and influence therapeutic outcomes of drugs commonly used to treat cardiovascular disease, cancer, central nervous system disorders, and pain (Dempsey et al., 2000; Benowitz et al., 2000).

Genetic polymorphisms within genes encoding drug targets, drug transporters and drug-metabolizing enzymes (DMEs) can affect the Pharmacokinetic (PK) and Pharmacodynamic (PD) characteristics of drug compounds (Steimer and Potter, 2002; Johnson, 2003). The therapeutic index of a drug (the difference between the minimum effective dose and maximum tolerated dose) and the quantitative role of a drug transporter or DME in the drug’s kinetics determine the clinical relevance of such genetic polymorphisms (Meyer, 2000) -e.g. the narrower a drug’s therapeutic index, the greater the clinical effects of changes in its PK and PD characteristics. The clinical effects of these genetic polymorphisms on the PK and PD of pharmaceutical drugs can lead to variable drug efficacy or risk of toxicity and ADRs.

The variations in clinical response to drug therapy ascribable to genetically-determined changes in drug PK (due to altered levels of drug metabolism) allow the classification of patients into four clinical groups (Sundberg, 1998; Sundberg et al., 1999; Meyer, 2000; Sundberg, 2004).

These four groups include:

  • Extensive metabolisers (EMs), who are either homo- or heterozygous for the wild-type or normal-activity enzymes and display a level of drug metabolism observed in the majority of patients;
  • Poor metabolisers (PMs), who carry two loss-of-function alleles and therefore have a severely impaired level of drug metabolism;
  • Intermediate metabolisers (IMs), who carry two decreased-activity alleles, resulting in decreased enzyme activity and subsequent level of drug metabolism (relative to EMs);
  • Ultra-rapid metabolisers (UMs), who have duplicated or multi-duplicated active copies of a gene and thus exhibit a considerably higher level of drug metabolism relative to EMs.

The two extremes of these four groups, namely PMs and UMs, clearly illustrate the clinical importance and effects of genotype on phenotype in terms of drug metabolism and response, as evident in Table 1.1

Table 1.1: The clinical effects of genotypic influences on phenotype in terms of drug metabolism (Ingelman-Sundberg, 1998; Bean, 2000; Ingelman-Sundberg, 2004).

Decreased rate of metabolism; increased drug bioavailability. Increased rate of metabolism; decreased drug bioavailability.
Exaggerated response at standard dosage; side-effects, toxic effects (ADRs). Lack of therapeutic effect at standard dosage; explanation for suspected poor adherence.
Active metabolite not formed (in case of a pro-drug); loss of therapeutic efficacy. Excess of active metabolite formed (in case of a pro-drug); side-effects, toxic effects (ADRs).

The consequences of either a markedly decreased or increased level of drug metabolism (or drug transport) can thus ultimately manifest in unintended and undesirable side-effects, or ADRs, and variations in levels of therapeutic efficacy.

1.5 Cytochrome P450 enzymes

The cytochromes P450 are a super family of enzymes that are found in all forms of living organisms. They are responsible for the metabolism of many endogenous compounds, participate in the activation/deactivation of many carcinogens and detoxify many xenobiotics. In particular, in humans they metabolize many drugs and hence are of great interest to pharmacologists and toxicologists. It is readily identified by a pronounced absorbance band at 450 nm in the soret region of the visible spectrum when the carbon monoxide adduct of the reduced heme protein is formed (Pohl et al., 1984) hence the name P450. Human cytochrome P450 (P450) enzymes catalyze the metabolismof a wide variety of clinically, physiologically, and toxicologicallyimportant compounds (Willson et al., 2001; Kliewer et al.,2002).

In this oxidation-reduction process, two microsomal enzymes play a key role. The first of these is a flavoprotein, NADPH-cytochrome P450 reductase. One mole of this enzyme contains 1 mol each of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). The second microsomal enzyme is a hemoprotein called cytochrome P450, which serves as the terminal oxidase (Meyer et al., 2000; Lancet, et al. 2000).

1.5.1 CYTOCHROME P450 families in Humans

Humans have 57 genes and more than 59 pseudogenes divided among 18 families of cytochrome P450 genes and 43 subfamilies. This is a summary of the genes and of the proteins they encode.

Table 1.2: Cytochrome P450 families in humans

Family Function Members Names
CYP1 drug and steroid (especially estrogen) metabolism 3subfamilies, 3 genes,1 pseudogene CYP1A1, CYP1A2, CYP1B1
CYP2 drug and steroid metabolism 13subfamilies,16genes,16 pseudogene CYP2A6, CYP2A7, CYP2A13, CYP2B6, CYP2C8, CYP2C9, CYP2C18, CYP2C19, CYP2D6, CYP2E1, CYP2F1, CYP2J2, CYP2R1, CYP2S1, CYP2U1, CYP2W1
CYP3 drug and steroid (including testosterone) metabolism 1 subfamilies, 4 genes,2 pseudogene CYP3A4, CYP3A5, CYP3A7, CYP3A43
CYP4 arachidonic acid or fatty acid metabolism 6 subfamilies, 11 genes,10 pseudogene CYP4A11, CYP4A22, CYP4B1, CYP4F2, CYP4F3, CYP4F8, CYP4F11, CYP4F12, CYP4F22, CYP4V2, CYP4X1, CYP4Z1
CYP5 thromboxane A2 synthase 1 subfamilies, 1 genes CYP5A1
CYP7 Bile acid biosynthesis 2 subfamilies, 2 genes, CYP7A1, CYP7B1
CYP8 varied 2subfamilies, 2 genes, CYP8A1 (prostacyclin synthase), CYP8B1 (bile acid biosynthesis)
CYP11 steroid biosynthesis 2subfamilies, 3 genes, CYP11A1, CYP11B1, CYP11B2
CYP17 steroid biosynthesis,17-alpha hydroxylase 1 subfamilies, 1 genes, CYP17A1
CYP19 Steroid biosynthesis: aromatase synthesizes estrogen 1subfamilies, 1 genes, CYP19A1
CYP20 unknown function 1 subfamilies, 1 genes, CYP20A1
CYP21 steroid biosynthesis 2 subfamilies, 2 genes,1 pseudogene CYP21A2
CYP24 vitamin D degradation 1 subfamilies, 1 genes, CYP24A1
CYP26 retinoic acid hydroxylase 3 subfamilies, 3 genes, CYP26A1, CYP26B1, CYP26C1
CYP27 varied 3 subfamilies, 3 genes, CYP27A1(bileacid biosynthesis),
CYP39 7-alpha hydroxylation of 24-hydroxycholesterol 1 subfamilies, 1 genes, CYP39A1
CYP46 cholesterol24-hydroxylase 1 subfamilies, 1 genes, CYP46A1
CYP51 cholesterol biosynthesis 1 subfamilies, 1 genes,3 pseudogene CYP51A1 (lanosterol 14-alpha demethylase)

(Danielson, 2002; Nelson, 2003).

CYP3A in drug metabolism

Cytochrome P450 3A is an iso-enzyme involved in Phase I oxidative metabolism of many endogenous and exogenous substances. From a quantitative point of view it is the most important hepatic CYP-enzyme, accounting for approximately 36% of all liver cytochrome P450s. Since CYP3A is also present in the small intestine, it has a significant effect on the first-pass metabolism of CYP3A substrates.

Most drugs are metabolized by CYP3A enzymes, and variations in expression levels of these enzymes are believed to determine whether patients will have a positive or adverse drug response. Little is known about the mechanisms that underlie inter-individual differences in CYP3A expression, but the mapping of human genome sequence variations will facilitate the search for answers (Eichelbaum & Oliver, 2001).

Figure 1.3: Proportion of drugs metabolized by Cytochrome P450

Figure 1.4: Role of CYP enzymes in hepatic drug metabolism

Genetic variability of CYP3A

The human CYP3A subfamily plays a dominant role in the metabolic elimination of more drugs than any other biotransformation enzyme. CYP3A enzyme is localized in the liver and small intestine and thus contributes to first-pass and systemic metabolism. CYP3A expression varies as much as 40-fold in liver and small intestine donor tissues.

The human CYP3A locus is comprised of four functional genes (CYP3A4, CYP3A5, CYP3A7 and CYP3A43), but the differentiation between their products has proven difficult, due to the similarities in their protein sequence, in antigenic properties and due to overlapping substrate specificities (Gellner et al., 2001). In consequence, even though the variability in the expression is established for the three most important CYP3A genes (CYP3A4, CYP3A5 and CYP3A7), their respective contributions to the hepatic CYP3A pool and their effects on drug metabolism are still a matter of debate.

Table 1.3: Contribution of each CYP3A enzymes to the total hepatic CYP3A protein pool.

CYP3A Contribution to the total hepatic ReferencesEnzyme CYP3A protein pool (%)

CYP3A4 40-98% (Wrighton et al., 1990; Tateishi et al., 1999; Kuehl

et al., 2001; Koch et al., 2002; Lin et al., 2002;

CYP3A5 2-60% Westlind-Johnsson et al., 2003).

CYP3A7 13%, 24% (Stevens et al., 2003; Sim et al., 2005).

CYP3A43 Not detected (Domanski et al., 2001; Gellner et al., 2001;

Westlind et al., 2001).

Unlike other human P450s (CYP2D6, CYP2C19) there is no evidence of a ‘null’ allele for CYP3A4. More than 30 SNPs (single nucleotide polymorphisms) have been identified in the CYP3A4 gene. Generally, variants in the coding regions of CYP3A4 occur at allele frequencies <5% and appear as heterozygous with the wild-type allele (Koch et al., 2002).

These coding variants may contribute to but are not likely to be the major cause of inter-individual differences in CYP3A-dependent clearance, because of the low allele frequencies and limited alterations in enzyme expression or catalytic function. The most common variant, CYP3A4*1B, is an A-392G transition in the 5′-flanking region with an allele frequency ranging from 0% (Chinese and Japanese) to 45% (African-Americans). (Lin et al., 2002)

Studies have not linked CYP3A4*1B with alterations in CYP3A substrate metabolism. In contrast, there are several reports about its association with various disease states including prostate cancer, secondary leukemias, and early puberty. Linkage disequilibrium between CYP3A4*1B and another CYP3A allele (CYP3A5*1) may be the true cause of the clinical phenotype. CYP3A5 is polymorphically expressed in adults with readily detectable expression in about 10-20% in Caucasians, 33% in Japanese and 55% in African-Americans. The primary causal mutation for its polymorphic expression (CYP3A5*3) confers low CYP3A5 protein expression as a result of improper mRNA splicing and reduced translation of a functional protein.

The CYP3A5*3 allele frequency varies from approximately 50% in African-Americans to 90% in Caucasians. Functionally, microsomes from a CYP3A5*3/*3 liver contain very low CYP3A5 protein and display on average reduced catalytic activity towards midazolam. Additional intronic or exonic mutations (CYP3A5*5, *6, and *7) may alter splicing and result in premature stop codons or exon deletion. Several CYP3A5 coding variants have been described, but occur at relatively low allelic frequencies and their functional significance has not been established. As CYP3A5 is the primary extrahepatic CYP3A isoform, its polymorphic expression may be implicated in disease risk and the metabolism of endogenous steroids or xenobiotics in these tissues (e.g., lung, kidney, prostate, breast, leukocytes).

CYP3A7 is considered to be the major fetal liver CYP3A enzyme. Although hepatic CYP3A7 expression appears to be significantly down-regulated after birth, protein and mRNA have been detected in adults. Recently, increased CYP3A7 mRNA expression has been associated with the replacement of a 60-bp segment of the CYP3A7 promoter with a homologous segment in the CYP3A4 promoter (CYP3A7*1C allele). This mutational swap confers increased gene transcription due to an enhanced interaction between activated PXR: RXRa complex and its cognate response element (ER-6).

The genetic basis for polymorphic expression of CYP3A5 and CYP3A7 has now been established. Moreover, the substrate specificity and product regioselectivity of these isoforms can differ from that of CYP3A4.


CYP3A4 encodes a member of the cytochrome P450 superfamily of enzymes. The cytochrome P450 proteins are monooxygenases which catalyze many reactions involved in drug metabolism and synthesis of cholesterol, steroids and other lipids. This protein localizes to the endoplasmic reticulum and its expression is induced by glucocorticoids and some pharmacological agents. This enzyme is involved in the metabolism of approximately half the drugs which are used today, including acetaminophen, codeine, cyclosporin A, diazepam and erythromycin. The enzyme also metabolizes some steroids and carcinogens. This gene is part of a cluster of cytochrome P450 genes on chromosome 7q21.1. Previously another CYP3A gene, CYP3A3, was thought to exist; however, it is now thought that this sequence represents a transcript variant of CYP3A4.

The four CYP3A genes encoding their respective enzymes are localized in a 231-kb cluster on chromosome band 7q21-q22.1 (Brooks et al., 1988; Spurr et al., 1989; Inoue et al., 1992) and reside in tandem, adjacent to each other in the order: CYP3A43-CYP3A4-CYP3A7-CYP3A5 (Nelson et al., 1996; Domanski et al., 2001; Gellner et al., 2001; Finta and Zaphiropoulos, 2002) in which the CYP3A43 gene is in a head-to-head orientation with its neighbouring gene CYP3A4, and the other three genes lie in head-to-tail orientation. Two pseudogenes (Nelson et al., 2004), CYP3AP1 and CYP3AP2 are present between the intergenic regions of CYP3A7-CYP3A5 and CYP3A4-CYP3A7, respectively (Finta and Zaphiropoulos, 2000).

The official name of CYP3A4 is cytochrome P450, family 3, subfamily A, and polypeptide 4. Identification of single nucleotide polymorphisms (SNPs) in the CYP3A genes has been an active area of research. Currently, 39 CYP3A4 alleles, comprising 65 SNPs have been reported.

Several alleles of CYP3A4 have been reported. By far the most common CYP3A4 genetic variant is the A-392G transition (CYP3A4*1B) located in the 5’-regulatory region (Rebbeck et al., 1998; Westlind et al., 1999). CYP3A4*2 has a change at codon 222, an amino acid substitution serine/proline. Another rare allelic variant in codon 455 designated CYP3A4*3 was found in a single Chinese subject. Three more novel variants of CYP3A4 were found in Chinese subjects (Hsieh et al., 2001). These alleles were designated as CYP3A4*4, CYP3A4*5 and CYP3A4*6. Seven more genetic variants were identified resulted in amino acid substitutions were designated as CYP3A4*7, CYP3A4*8, CYP3A4*9, CYP3A4*10, CYP3A4*11, CYP3A4*12, and CYP3A4*13 (Eiselt et al., 2001). Three new coding-region polymorphisms CYP3A4*17, CYP3A4*18 and CYP3A4*19 are also identified (Die et al., 2001).

CYP3A4*1B allelic frequency varies among different ethnic groups: 0% in Asian, 5% in Caucasians and 54% in Africans (Chowbay et al., 2005). CYP3A4*1B demonstrates a frequency of 60% & 4% in Africans and Caucasians respectively and is absent in Chinese & Japanese (Ball et al., 1999; Sata et al., 2000). The other allelic variants occur at much lower frequencies (<1%-2%) or they are selectively prevalent in specific populations (Hamzeiy et al., 2002; Lamba et al., 2002; Floyd et al., 2003). CYP3A4*2 occurred with a frequency of 2.7% in Caucasians and is absent in Africans and Chinese (Sata et al., 2000). Another rare allelic variant in codon 455 designated CYP3A4*3 was found in a single Chinese subject. CYP3A4*4, *5 and *6 occurred with a frequency of 1%, 0.9% and 0.5% respectively in 102 Chinese subjects (Hsieh et al., 2001). CYP3A4*7, *8, *9, *10, *11, *12, and *13 alleles were reported in Caucasians with a frequency of 1.41%, 0.33%, 0.24%, 0.24%, 0.34%, 0.34% and 0.34% respectively (Eiselt et al., 2001). CYP3A4*15, *17, *18, and *19 alleles were reported in Caucasians, Chinese, Africans, and Indo-Pakistanis (Dai et al., 2001).

Chowbay et al. reported the absence of CYP3A4*1B, *4, *5 and presence of CYP3A4*6 In Indians residing in Singapore. Another study demonstrates the absence of genetic polymorphism of CYP3A in North Indians on the basis of frequency distribution of urinary 6?-hydroxy-Cortisol/ Cortisol ratio and the absence of variant alleles CYP3A4*2, *4, *5, *6 and *10 (Rais et al., 2006).

Many studies have been carried out on functions of CYP3A4 variants. CYP3A4*4 has been studied to ascertain the effect of the mutation on transcriptional activity and in vivo catalytic activity (Amirimani et al., 1999; Ando et al., 1999). The results of studies with larger numbers of predominantly Caucasian liver samples demonstrate no clear association between CYP3A4*4 variant and CYP3A4 specific content or catalytic activity (Westlind et al., 1999; Lamba et al., 2002).

Compared with wild-type enzyme, there was no significant difference in the rates of CYP3A4*3, CYP3A4*7, CYP3A4*9, CYP3A4*11 and CYP3A4*19 metabolizing the probe substrates testosterone, progesterone, or 7-benzyloxy-4-(trifluoromethyl) coumarin (Sata et al., 2000; Dai et al., 2001; Eiselt et al., 2001). It means that these variants have no pronounced effect on drug metabolism kinetics. The individuals with CYP3A4*8 and CYP3A4*13 genotypes may have lower CYP3A4 protein content, since these variants appear to affect steady-state enzyme levels by altering heme binding and/or protein stability (Eiselt et al., 2001).

For CYP3A4*2, CYP3A4*10, CYP3A4*14, CYP3A4*15 and CYP3A4*16, there was no significant association with midazolam hydroxylation activity (Lamba et al., 2002). Those with CYP3A4*17 genotype exhibited in vitro a significantly lower turnover of testosterone and of the insecticide chlorpyrifos than those with CYP3A4*1, while those with CYP3A4*18 metabolized both substrates with a higher turnover (Dai et al., 2001). These catalytic findings and consideration of the low allele frequencies for the known structural CYP3A4 variants, implies that they are not the major cause of interindividual differences in CYP3A-mediated drug clearance in the general population.


Human Cytochrome P4503A4 is a major P450 enzyme in the liver and gastrointestinal tract. It is responsible for the metabolism of more than 60% drugs used by the human including clinically important drugs like nifedipine, cyclosporine, erythromycin, midazolam, triazolam, digitoxin, lidocaine, quinine etc., some endogenous steroids, and harmful environmental contaminants. A number of allelic variations in CYP3A4 gene are known to affect catalytic activity including CYP3A4*1B, CYP3A4*2, CYP3A4*4, CYP3A4*5, CYP3A4*6, CYP3A4*10 and CYP3A4*18. Among Asian subjects, a number of allelic variations in CYP3A4 gene are known to affect catalytic activity including CYP3A4*2, CYP3A4*4, CYP3A4*5, CYP3A4*6, CYP3A4*10, CYP3A4*1B, CYP3A4*18.

The purpose of this study is to characterize the genotypes CYP3A4*4 in Nepalese peoples, which will be helpful for the adjustment of dosage regimen, reduce the serious adverse drug reactions to ensure safe, effectiv