Pharmacogenomics: From Discovery to Clinical Implementation

Pharmacogenomics: From Discovery to Clinical Implementation is a complete reference aimed at building a solid foundation of the key concepts in this fast-moving knowledge area.

The editors lined up a group of worldwide experts to contribute to the book. Following a consistent chapter structure, the authors cover the foundational aspects of pharmacogenomics in the first four chapters of the book that include basic concepts, drug metabolism, drug discovery and development, and testing. The second part of the book is dedicated to detailed studies of key health conditions and the potential therapeutic applications of pharmacogenomics. Diseases covered include diabetes, cardiovascular diseases, psychiatric disease, cancer, pulmonary and respiratory diseases, viral diseases, gastroenterology, autoimmune diseases, immunosuppressants, and, finally, an overview of computational resources.

Pharmacogenomics: From Discovery to Clinical Implementation is the perfect resource for pharmaceutical science graduate students to learn the key concepts of the area. Researchers and graduate students in the related fields of Genetics, pharmacoepidemiology, molecular biology, and medicinal chemistry will also benefit of the structured approach of the book.

• Provides an in-depth review of pharmacogenomics and its role in drug discovery/metabolism and its clinical impacts
• Describes the practice of pharmacogenomics in the treatment of diabetes, cancers, cardiovascular diseases, psychiatric disorders, pulmonary diseases, infectious, gastroenterology, and autoimmune diseases
• Uses a consistent chapter structure to support understanding of the fundamental concepts in the area

• Language: English
• Publisher: Academic Press
• Release date: Jun 13, 2023
• ISBN: 9780443153372

Book preview

Section 1

Fundamentals

Chapter 1: Basics of pharmacogenomics

Ina Amina; Aarif Alia; Ishteyaq Majeed Shahb; Rasy Fayaz Choh Wanib; Farhat Jabeena; Hilal Ahmad Wanic; Saima Mushtaqd; Muneeb U. Rehmane; Mir Tahir Maqboolf    a Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, Jammu and Kashmir, India

b Advanced Research Lab, Department of Zoology, School of Biological Sciences, University of Kashmir, Srinagar, Jammu and Kashmir, India

c Department of Biochemistry, Govt. Degree College (Sumbal), Sumbal, Jammu and Kashmir, India

d Veterinary Microbiology Department, Indian Veterinary Research Institute (IVRI), Bareilly, Uttar Pradesh, India

e Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia

f National Centre for Natural Products Research, University of Mississippi, Oxford, MS, United States

Abstract

Pharmacogenomics involves the understanding of how genetic variation affects a patient’s response to a specific medication. The field of pharmacogenomics possesses immense potential to influence relevant outcomes in the dosing, toxicity, and efficacy of drugs. In the testing of many commonly used drugs, pharmacogenomics has provided substantial evidence, and the involvement of genetic and nongenetic factors could be a probable reason that could determine the clinical relevance of medications. Therefore the detection of genetic markers that are related to drug responses will not always associate with useful clinical predictors of adverse outcomes. Genetics plays a significant role in the regulation of pharmacodynamic and pharmacokinetic responses. In fact, pharmacogenetics is the exploration of genetic differences that leads to variance in drug responses, including polymorphism at the gene level of drug transporters, drug-metabolizing enzymes, and drug receptors. The absence of readily accessible resources, utility, cost-effectiveness, feasibility, evidence level, and social, ethical, and legal issues further confines and challenges the implementation of pharmacogenomics in the clinical field. The implication of a genetic marker in clinical practice requires the association of that marker with a specific trait. However, to overcome these hindrances, pharmacogenomic testing of drugs that are presently used in clinical practice could be applied in determining adverse drug effects and efficacy. The cost of high-efficiency sequencing and genotyping has fallen sharply, and companies are now routinely trying to perform complete genetic characterization of clinical trial candidates. In PGx investigations, the frequent usage of polygenic risk scores is increasing in regard to scientific interest. Different types of genetic variation can supply pharmacological targets with useful information.

Keywords

Pharmacogenomics; Drug; Ethics; Biomarkers; Drug response; Challenges

1.1: Introduction

Pharmacogenomics is a recent scientific discipline that encompasses both genomics (the study of genes) and pharmacology (the study of drugs) to develop safe medication with relevant doses based on the genetic profile of an individual. The pharmacogenomic slogan is to create personalized medicines that are right medicine for right person at right doses. Although the field is naive but growing at a large pace, the first report regarding dose recommendations for drugs meant for psychiatry and to treat depression was made even before when the first human genome was sequenced (Kirchheiner et al., 2001). The present-day medicine works on the principle of one medication fits all, but after recent advances in modern-day technology including great projects such as Human genome, ENCODE, and FANTOM, the systematic annotation and functional depiction of genes have shown that about 80% of the human genome is actively transcribed and there are variations among the genes (called alleles) and the same drugs might not work the same way for everyone. The same drugs might not work the same way for everyone. The human genome largely consists of some ∼20k protein-coding genes. Perhaps the most important variation that exists between individuals is the single-nucleotide polymorphism (SNP). Accordingly, there are some 22 million SNPs in the complete human genome (NCBI Resource Coordinators), and these variations are categorized into nonsynonymous SNPs and synonymous SNPs. Nonsynonymous SNPs can alter protein activity as such substitution occurs in the coding region and has huge ramification for drug responses that rely on the protein for transport, metabolism, or evoking cellular responses. Synonymous SNPs on the other hand do not result in amino acid substitution. However, they may result in alteration in gene expression and the amount of protein formation, if they occur in the gene regulatory region including the promoter region. There is a high probability of two or more SNPs coinheriting at a higher rate than what could be expected by chance alone. Other kinds of variations include short tandem repeats, multiple-base nucleotide variations, and insertion-deletion polymorphisms (indels). Polymorphism in genes encoding drug transporters, drug metabolism, and drug target proteins is very much frequent.

Predicting the level of effectiveness or success rate of a drug for a particular person is very much difficult, attributed to variability in drug therapy response among individuals. There are a number of factors such as age, body size, renal functioning, liver functioning, and sex that contribute to influencing drugs response. The terms pharmacogenetics and pharmacogenomics are interoperable. However, pharmacogenomics explores the entire set of genes that are associated with a drug including safety and effectiveness, whereas the term pharmacogenetics refers to monogenetic iterations that alter drug response. Therefore, the field of pharmacogenomics deals/investigates the role of genes in drug response and explores the capacity to achieve individually tailored drugs—the right medicine, for the right patient, at the right dose.

Given the recent advancement in science and technology, the dream of personalized medicines, which was imagined years back, has not come to fruition. According to Nelson et al. (2011) and Pulley et al. (2012), the delay is attributed to oncology (primarily) and now recently to cardiology, where genotyping to assess clopidogrel efficacy is beginning to become commonplace at some significant academic medical centers. To explain heterogeneity in pharmacokinetic responses to pharmacological therapy, pharmacologists were traditionally focused on genetic variability. Nonetheless, there are transport proteins and receptors (critical proteins) that also get impacted by genetic diversity. Therefore, a better way to define pharmacogenetics is the exploration of genetic differences that lead to variance in drug responses, including polymorphism at the gene level of drug transporters, drug-metabolizing enzymes, and drug receptors.

Genetic factors that affect drug response can be categorized into two groups: the first group represents those that influence drug metabolism, for example, those caused by variations in N-acetyltransferase concentrations/activities or atypical plasma cholinesterase, and the second group represents those that impact pharmacodynamics. More frequently than pharmacodynamics, drug metabolic variability causes interindividual variance in therapeutic medication response and toxicity. Monogenic or polygenic genetic variation in drug metabolism has been observed. Monogenic describes heterogeneity that results from a single gene. The term polygenic refers to genes that have minimal individual effects but have large impacts when they are combined. According to Inaba et al. (1995) the majority of pharmacogenetic research focuses on polymorphisms (i.e., differences that occur in at least 1% of the population) and single-gene versions of drug-metabolizing enzymes. Such genes typically influence how drugs are transformed by changing the amount or performance of an enzyme.

1.2: Drug metabolism

Drug metabolism is been categorized into phase I and phase II reactions and mostly occurs in the liver. While phase II is a type of conjugation reactions, a process that combines the medication with an endogenous hydrophilic component to produce more water-soluble molecules, phase I reactions, such as oxidation, reduction, and hydrolysis, add a polar group into the molecule.

Phase I enzymes (oxidation), one of the main avenues of metabolism for many medicines, are carried by the mixed function oxidase system, consisting of P450 (CYP) enzymes. According to Slaughter and Edwards (1995) and Tanaka (1999), phase I reactions of P450 enzymes have their appearances in almost all the tissues with the endoplasmic reticulum of liver possessing the highest proportion. The most significant and well-researched group of metabolic enzymes that has clinically significant genetic variations is the cytochrome P450 (CYP) superfamily of isoenzymes. Within this superfamily of isoenzymes, some 58 pseudogenes and 57 different CYP genes have been categorized into 18 families and 44 subfamilies. Varying degrees of sequence similarity based on the homology/similarity of their amino acid sequences have been found, 15 of these genes and pseudogenes are known to be involved in the metabolism of drugs in humans, and 42 are involved in the metabolism of exogenous xenobiotics and endogenous substances, such as steroids and prostaglandins (Zanger et al., 2008). Liver drug metabolism involves many variants of P450. Drugs or xenobiotics found in the environment can either hinder or stimulate these actions. The genetic basis of their function is also influenced by frequent polymorphisms. Because of the extreme polymorphism of CYP genes, various isoenzymes, including CYP2B6, CYP2C9, CYP2A6, CYP2C19, CYP3A4/5, and CYP2D6, have functional genetic polymorphism. The end result of these CYP variations is amino acid alterations, gene duplications, premature stop codons or splicing errors, harmful mutations, and gene deletions.

Depending on the differences in the number of functional variants of genes encoding a particular CYP and expression of enzyme activity, patients are categorized into four metabolic phenotypes, poor metabolizers having faculty or even deleted alleles and disbanded activity. The second class of patients is those who have reduced/diminished enzyme activity and have only one functional and one defective allele for CYP, these are known as intermediate metabolizers. The third class is known as normal metabolizers, carries both functional copies, and has normal enzyme activity. The fourth and the last one is ultra-rapid metabolizers, having gene variation that has been duplicated producing more than two/multiple copies and having extreme enzyme activity. The most pertinent CYP genetic polymorphisms at the moment are those affecting CYP2D6, CYP2C19, and CYP2C9.

Particular drug metabolism phenotype arises because of genotyping that involves exploration/identification of specific genetic variants on the CYP genes. The resultant outcome is gene duplication (i.e., elevated expression) and abrogated/no protein product (null allele) or may be the formation of mutant protein with lower expression/activity (inactivating allele). Genotyping that can be done once in a lifetime requires a small amount of blood or tissue and is usually unaffected by the medication or underlying condition. The phenotype of drug metabolism in an individual can be identified by testing for genetic variations (van der Weide & Steijns, 1999). Other phase 1 enzymes that are polymorphic in addition to P450 genes include examples, such as acetaldehyde dehydrogenase (ALDH), alcohol dehydrogenase (ADH), and dihydropyrimidine dehydrogenase. The clearance of ethanol is greatly impacted by the first two enzymes, with ALDH2 polymorphism affecting acetaldehyde metabolism and ADHB2 giving a greater rate of ethanol metabolism. When consuming ethanol, people with poor ALDH2 metabolizers experience flushing and antiabuse-like side effects, and there are fewer alcoholics with this genotype. In DPD, a polymorphism is pertinent to the use of anticancer medications. This enzyme metabolizes 5-fluorouracil. Those who have inactivating gene mutations that impede enzyme activity are at significantly higher risk of experiencing negative side effects, such as myelotoxicity and neurotoxicity, after the administration of 5-fluorouracil (Ingelman‐Sundberg, 2001).

1.2.1: CYP2D6

The only drug-metabolizing enzyme that cannot be induced and genetic variations are largely to blame for the substantial interindividual variances in enzyme activity. CYP2D6 is one of the best studied among all variants with 100 gene variants and near about 120 alleles. Sistonen et al. (2007) demonstrated that characterizing genotypically 12 SNPs to establish 20 different haplotypes could reliably predict the genuine phenotype with 90%–95% accuracy even with a huge number of alleles. Among the many enzymes, CYP2D6*1 and CYP2D6*2 show suitable enzyme activity. The two most significant null variations are CYP2D6*4 (c.1846G>A, rs3892097) and CYP2D6*5 (gene deletion), which result in an inactive enzyme and an absence of enzyme, respectively. CYP2D6*41 (c.2988G>A, rs28371725), CYP2D6*10 (c.100C>T, rs1065852), and CYP2D6*17 (c.1023C>T, rs28371706) are frequently linked to significant reductions in enzyme activity, which are phenotypically expressed as IM. While only making up just 2% of total CYP content in the liver, it modulates near about 20%–30% of commercially available drugs. CYP2D6 variation greatly impacts the disposal of around 50% of these drugs.

1.2.2: CYP2C19

When evaluated for clinical implication, the CYP2C19 gene does not module many medicines compared with CYP2D6 polymorphism. From many past years near about 15 CYP2C19 alleles have been explored (including *2, *3, *4, *5, *6, *7, and *8). Among all CYP content of the liver, CYP2C19 accounts for about 3% and CYP2C19 polymorphism targets are mostly proton pump inhibitors.

1.2.3: CYP2C9

Some 10% of the medications that are currently available in markets are metabolized by CYP2C9. CYP2C9 constitutes 20% of total hepatic CYP content. As of now, some 35 alleles have been characterized in both the coding and regulation regions of CYP2C9. Among the most studied are CYP2C9*2 and CYP2C9*3.

1.2.4: Transporter proteins

Drugs are carried inside the cell either through passive diffusion or through transporters (active diffusion). According to Klein et al. (1999), transport through transmembrane proteins plays a key role in affecting drug bioavailability (member of ABC proteins) and may work in conjunction with intracellular drug-metabolizing enzymes. Among the most characterized ABC proteins is P-glycoprotein expressed naturally on the plasmatic membranes of endothelial cells at blood-brain barrier (BBB) that may have an impact on how well substrates are absorbed into the brain. High P-glycoprotein levels will limit the brain from receiving enough of the desired drug whereas low P-glycoprotein levels may lead to elevated levels of abnormal drug accumulation and bringing unfavorable side effects. Among the subfamily of ABC transporters known as multidrug-resistant associated proteins is MRP1 whose over expression is associated with the majority of non-P-glycoprotein-mediated multidrug resistance.

1.2.5: Receptors

The receptor is the most common immediate pick for genetic analysis of drug response while evaluating the reaction to a drug. Genetic variation affects receptor interactions, which is what causes ineffective or effective receptor interactions. The polymorphisms in genes encoding receptors important for the therapeutic therapy of many disorders result in substantial diversity in medication sensitivity.

For instance, using oral contraceptives may raise the risk of cerebral vein thrombosis in people who have a mutation in the prothrombin gene. Angiotensin-converting enzyme (ACE) and its sensitivity to ACE inhibitors, b-adrenergic receptors and their responsiveness to b-agonists in asthmatics, and 5-hydroxy-tryptamine receptors and the responsiveness to specific neuroleptics are other examples of the effects of genetic polymorphisms.

1.3: Ethics in pharmacogenomics

To have the best or to achieve optimum effectiveness with the least side effects, pharmacogenomics strives to explore and create reasonable methods to optimize drug therapy with due respect and attentiveness to the genotype of the patients. It is envisaged that the use of pharmacogenomics will allow for a unique approach to pharmaceutical medication treatment. Pharmacogenomics aimed to do away the trial-and-error method of prescription. Pharmacogenomics allowed physicians to consider the genes, their functionality, and the outcome on patients’ present and future effectiveness. It also allowed to provide an interpretation of the malfunction of preceding therapies. These strategies signal the looming of precise and customized medicine whereby drug combos and dosages are tailored to certain patient subgroups or even to each person’s unique genetic profile. The main outcome of the strategy is to limit the adverse effect of drug toxicity and increase efficacy. Two alternative sources of information can be utilized to produce pharmacogenomic prescriptions for a specific medication: genotyping or whole-genome sequencing (Sheffield & Phillimore, 2009).

Biomedical research is subjected to the number of guidelines from time to time. A variety of rules have been established over time to guarantee that biomedical research is conducted in an ethical manner. The Declaration of Helsinki, released by the World Medical Association in 1964, is the most significant of them all. Universal Declaration on Bioethics and Human Rights released by United Nations Educational, Scientific, and Cultural Organization (UNESCO) is the latest release among the declarations and it addresses the bioethics, human genome, human rights, and genetic information. This proclamation goes into great length about pharmacogenomics’ ethical implications for global health in developing nations. The guiding principles with respect to bioethics are summarized as follows:

(1)The categorical imperative needs to refrain from doing harm to other people through our actions (nonmaleficence).

(2)Respect for other people as generally good.

(3)Need to help people by making sure that our actions are always calculated to have more positive than negative effects.

(4)The requirement to treat other people fairly, without excessively exploiting them or in any other way misleading them (justice).

These guidelines offer a platform for debating moral issues in pharmacogenomics. Thus to choose the most acceptable course of action, each ethical issue must be evaluated in the context of balancing these principles (Beauchamp & Childress, 2001; Parekh, 2007).

1.3.1: Public policy

Based on an evaluation of reliability, effectiveness, and security, the Medicines and Healthcare products Regulatory Agency (MHRA) is in charge of licensing new medications and genetic tests. Its purview will also include the authorization of pharmacogenetic diagnostics and medications. These tests must be trustworthy and of the highest caliber. According to Lipton (2003), in certain circumstances, a drug cannot be permitted unless it is used in combination with a diagnostic. Since healthcare professionals have minimal resources, pharmacogenetics may provide data that are pertinent to such appraisals by providing more reliable assessment of the cost of medication. Pharmacogenetic data may have an impact on decisions regarding the cost-effectiveness of treating various patient populations with the same medication, but this thing may lead to a certain group of people with rare genetic disorders or variation/polymorphism (genetic) may not receive treatment if cost-effectiveness is the only consideration. Equality and justice must be taken into account. Occasionally, to promote a more equitable allocation of medical services, it may be appropriate to devote resources to illnesses or therapies that may not instead be deemed cost-effective.

1.3.2: Open consent vs informed consent

Huge patient investigation is needed to explore and duplicate therapeutic responses and adverse effects beyond correlations with drug-metabolizing enzymes. It is most common that patients who donate blood to a hospital frequently do not give their agreement for the sample to be used for further additional research or even the samples they give for testing. However, consent is not required generally on laboratory specimens including specimens meant for autopsy in the United States as per current ethical norms, but at the same time, many major problems would arise if the identity of the patient is not kept hidden. The practice of allowing research on current laboratory and pathological specimens collected for medical testing must continue, and there must be a solution on changing the rules governing the approval/consent that may give free liberty to research/examination to have open-ended consent, attempting the policy of instituting more stern disincentive sanctions for privacy violations, and so on. The United States Genetic Information Non-discrimination Act of 2008 forbids discrimination based on individuals’ genetic identity in issues related to insurance and employment and may act as a legal guide and as a model for the penalties for improper use of a person’s genetic information.

1.3.3: Ethnic variation

Even though the field gives promising results and is on the front run, there are certain factors impeding the further advancement of the discipline; one major controversy is regarding the use of ethnicity and race in pharmacogenomics. Regarding therapeutic efficacy, patient communities may also be divided based on racial or ethnic groups, since there are certain genetic variations that are prevalent in certain groups and this brings complications in design of clinical trials and ultimately public health decision.

Since there are significant genetic variations present between and within ethnic groups, it is hopefully that pharmacogenetics would offer a more precise method of predicting response to a drug rather than solely on ethnic classification. However, Wilson et al. (2001) demonstrated that population genetics research has shown that there is a significant amount of genetic variation within populations. Many issues surround the use of ethnicity in genetic studies. Despite this debate, the data point to a possible connection between ethnicity and the variability of pharmacological response.

1.3.4: Clinical issues

Thanks to the development of pharmacogenetics, a large number of people are going for genetic screening than before. However, easy access to the information and creditability of source is an x-factor in pharmacogenetics. Nonetheless, there have been discussions/heated arguments with respect to whether consent form (in writing) or genetic counseling for patients undergoing pharmacogenetic studies is required. Which information should be provided and if the test also discloses any extra information, such as susceptibility to a different disease or the likelihood of responding to other medications, are potential considerations. However, if testing was accessible and affordable, it would raise moral concerns about the level of informed consent needed to conduct pharmacogenomic testing for the purposes of prescribing drugs and the dangers associated with secondary data gleaned from pharmacogenomic testing in the clinical context.

1.4: Pharmacogenomics—Its present and future prospects

For the optimization of drug therapies deployed on a patient’s genomic profile, pharmacogenomics is utilized to reveal functionally significant genomic determinants for drug disposition and reaction. Pharmacogenomics has progressed from single-candidate gene research to wide-ranging genome-wide approaches and is used in the development of anticancer drugs, as well as the customized cancer therapy. In addition, the pharmacogenomics has its critical role in patients with diseases, such as the human immunodeficiency virus (HIV) and heart problems. It is also utilized to diagnose and treat persons with certain mental disorders such as Alzheimer. Furthermore, pharmacogenomic methods are being explored to look for medications that target specific molecular and cellular pathways involved in various diseases. It may potentially give some drugs a second chance after being shelved during the development process. The reposition from pharmacogenomics to clinical practice appears to be critical, and clinicians play an important part in this procedure. Pharmacogenetic studies can be performed in medical settings (point-of-care testing, or POCT) or in commercial labs, which will necessitate a significant contribution to substructure and training.

As the field of pharmacogenomics is expanding, and clinical studies are testing novel approaches, the pharmacogenomics will be utilized to generate tailor-made therapeutics to treat a wide range of health issues in the future.

1.5: Current therapeutics and pharmacogenomics

Medicine or drug development is an extremely costly procedure, costing between €500 and €800 million for each marketed drug. Each drug takes roughly 10–15   years to reach the market when it is discovered (Hewitt, 2001). Furthermore, there is a significant attrition rate, with only one out of every 5000 or even 10,000 chemical compounds thought to have therapeutic promise making it to clinical trials. Incorporating pharmacogenomics into the drug development operation has the potential to increase target discovery, speed up the process, and lower attrition rates. Pharmaceutical businesses and healthcare systems must account for individual differences in drug response, both in terms of efficacy and toxicity (Evans & Johnson, 2001). For example, the dose of warfarin necessary to produce optimum anticoagulation varies by 20 times among individuals. Polymorphisms in drug-metabolizing enzyme genes are linked to a higher risk of adverse drug responses (Phillips et al., 2001). A vast number of medicines implicated in adverse drug responses are processed by at least one enzyme that has a variant allele linked to lower activity. Moreover, there is a good chance that adverse drug reactions are caused by a combination of genetic factors. Furthermore, both hereditary and nongenetic variables are invariably responsible for adverse reactions and efficacy. The potential benefits, both in terms of health and economics, are substantial. Therapeutic intervention based on an individual’s genetic variation, on the other hand, will not be appropriate to all medications, and thorough cost-effectiveness analysis will be required on a case-by-case basis (Phillips et al., 2001; Veenstra et al., 2000). By selecting the right treatment for the right patient in the right disease at the appropriate dose, pharmacogenomics has the ability to enhance efficacy while also lowering toxicity in clinical practice.

1.6: Pharmacogenomics—Drug metabolism and development

Proteomic and genomic technologies might improve the variety of target medicinal agents by finding novel proteins, targeting proteins with variable structures, detecting pharmacological modes of action, generating compounds, and advancing the specificity of therapeutic action. The metabolism of pharmaceuticals in numerous human organs, most notably the liver, is separated into three stages. Phase I involves the introduction of reactive or polar groups into the pharmaceutical substance by enzymes, such as cytochrome P450 oxidases. The reaction mechanism of P450 oxidases involves the reduction of cytochrome-bound oxygen and the formation of a highly reactive oxyferryl species (Guengerich, 2001; Schlichting et al., 2000). Oxidation, reduction, hydrolysis, cyclization, decyclization, and addition of oxygen or removal of hydrogen, all of these phase I chemical reactions, are carried out by oxidases in the liver. In phase II reactions, the changed molecules are conjugated into polar compounds. Transferase enzymes, such as glutathione S-transferases, catalyze the aforementioned processes. Phase III involves further processing of conjugated medicines before they are identified by efflux transporters and pushed out of cells (Akagah et al., 2008). Clinical trials involving novel medications, on the other hand, are generally divided into four separate and unique phases. The drug development process, in most circumstances, will take several years to complete all four phases. If a drug effectively passes stages I, II, and III, it is generally permitted for utilization in the general population by the national regulatory agency. Pharmacogenetics may help refine phase I trials by focusing on people with genotypes that have been determined through preclinical research. Early detection of difficulties could result in the compound being dropped at phase I rather than phase III, saving money on development expenditures. There might be more modification of the pharmacogenetic factors of drug response in phase II that could give information for phase III study design. Although the sample size for phase III research might be lowered, there is a chance that more people would be investigated in stages I and II (Brazell et al., 1998). Table 1 summarizes the expected benefits of pharmacogenetic applications in clinical trials. Pharmaco-epidemiological investigations take place in the meantime, and they can last for the entire time the drug is on the market. Rare adverse effects that occur in this phase can be detected in phase IV. Patient samples may be utilized for pharmacogenetic testing and the recognition of genetic predisposing variables, permitting the risk-benefit ratio to be improved (Brazell et al., 1998).

Table 1

1.7: Biomarkers in pharmacogenomics/genetic variations and drug response

1.7.1: SNPs-CYPs

The classic example of a drug-responsive cancer in children is acute lymphoblastic leukemia (ALL). In the industrialized countries, more than 80% of children with ALL is cured with the aid of many modern risk-directed therapies. Antileukemic drugs, on the other hand, might produce serious side effects like detrimental drug reactions. Furthermore, many children have leukemia cell clones resistant to antileukemic therapy (Kager, 2009). The first research looked at how SNPs in genes encoding drug-metabolizing enzymes affected treatment outcomes. Individual reactions to the efficacy and toxicity of many drugs have been found to be very variable, due to the identification of SNPs. The expanding importance of pharmacogenetic molecular diagnostic testing is becoming more widely recognized as vital in improving drug therapy (Evans & Relling, 1999). One of the most well-known genotype-phenotype associations is the thiopurine methyltransferase (TPMT) gene and its impact on thiopurine therapy for acute lymphoblastic leukemia and immunological modulation (Relling et al., 2006; Schwab et al., 2002). Patients with TPMT deficiency are given 10–15-fold lower dosages of these drugs (Andersen et al., 1998; Evans et al., 1991; Lennard et al., 1993; McLeod et al., 1993), and they experience severe hematological toxicity that prevents the use of other therapies and can be lethal (Schütz et al., 1993). The TPMT genotype or phenotype could be utilized to recognize individuals at increased risk of hematological toxicity after thiopurine therapy, which has sparked clinical interest in TPMT pharmacogenomics (Lennard et al., 1993). The effect of pharmacogenetics on analgesia has been investigated so far (Drendel, 2007; Stamer & Stüber, 2007). The hepatic cytochrome P450 gene CYP2D6, which catalyzes the metabolism of several drugs, is a candidate gene. The analgesic codeine, a prodrug that is essentially bioactivated by CYP2D6 to morphine, a potent opioid agonist, is one drug whose metabolism is tightly linked to CYP2D6 genotype or phenotype. CYP2D6 polymorphisms have been observed to affect the effectiveness and safety of codeine (Table 2) (Kirchheiner et al., 2007; Lötsch et al., 2009). Table 2 shows the number of P450 genetic variations associated with drug metabolism. Drug efficacy is regulated by polymorphisms in genes that encode drug receptors, transporters, and drug targets, as well as differences in drug-metabolizing genes. A frequent promoter mutation in the molecular target of warfarin (VKORC1), for example, has a significant impact on the dose levels needed by individual patients. Similarly, pharmacogenomic connections with drug pharmacokinetics or effects have been discovered for a number of transporters. A synonymous SNP in the ABCB1 gene, for example, is being linked to the maximum digoxin concentration that may be achieved (Hoffmeyer et al., 2000). Similarly, polymorphisms in the SLCO1B1 transporter have been linked to a variety of phenotypes, including the higher risk of simvastatin-induced myopathy (SEARCH Collaborative Group, 2008), methotrexate-related gastrointestinal toxicity (Treviño et al., 2009), and flavopiridol disposition (Ni et al., 2010).

Table 2

Key term

Pharmacogenetics is often described as the variability in drug response due to genetic variability

Key term

Drug Efficacy is defined as the ability of a drug to produce maximum response (effect)

Other investigations have linked a CYP2C19 polymorphism to decreased clopidogrel platelet response (Shuldiner et al., 2009) and a CYP2C9 variant to warfarin dose demands (Table 3) (Cooper et al., 2008). SNPs in the interleukin 15 gene have been linked to antileukemic drug disposition in acute leukemia (Yang et al., 2009). Antiretroviral therapy is linked to polymorphisms in the cytochrome P450 gene CYP3A (Lakhman et al., 2009). Due to elevated enzymes, CYP3A induction causes an increase in the metabolism of the supplied drug. This can result in side effects, such as liver inflammation (hepatitis) (Table 3) (Willson & Kliewer, 2002). CYP3A is a polymorphic enzyme that metabolizes a variety of drugs used in highly active antiretroviral treatment regimens. The link between CYP3A4 and indinavir/atazanavir has been studied using pharmacogenomic techniques. TNF-α variation G>A at position -308 is linked to IgE-mediated beta-lactam hypersensitivity. The TNF-α GG genotype, together with total IgE level, was a strong independent predictor of primary risk of beta-lactam allergy. Polymorphisms in TNF-α are linked to a higher risk of beta-lactam allergy (Table 3) (Guéant-Rodriguez et al., 2008).

Table 3

1.8: Cancer therapy: Monoclonal antibodies

Monoclonal antibodies are used to treat cancer. Cancer is becoming a more prevalent health issue around the world. The World Health Organization reports more than 10 million cancer cases per year. During the three distinct but closely related stages of initiation, promotion, and progression, cancer is caused by a multistage, multimechanism carcinogenetic procedure that includes mutagenic cell death and epigenetic pathways. Because eliminating premalignant cells before they turn malignant is impossible during the initiation phase, the most potent intervention might be during the promotion phase (Karikas, 2010, 2011; Trosko, 2005). Carcinogenesis is a complicated process that involves the genetic and epigenetic variables that play a critical part in the growth of cancer. Dietary and environmental factors can easily alter epigenetic changes, such as DNA methylation, histone modifications, and posttranscriptional gene control by noncoding miRNAs (Karikas, 2012). These processes affect all transcript stability, nucleosome location, and full nuclear structure of the genetic material. They work together in a synergistic and cooperative manner to find out if a gene is silenced or expressed, as well as time and tissue specificity of its expression (Ellis et al., 2009). In humans, DNA methylation is a well-studied epigenetic signature that distinguishes normal cells from malignant cells. CpG islands in front of tumor suppressor gene promoters are frequently hypermethylated in cancer cells, whereas CpG methylation of oncogene promoter regions and parasite repeat sequences is frequently decreased. Normal cells, on the other hand, have unmethylated CpG islands (Esteller, 2007). miRNAs control around 60% of the transcriptional activity of protein-coding genes in mammals. In cancer cells, methylation-associated silencing of certain miRNAs has also been discovered (Lujambio et al., 2007; Saito et al., 2006). Given the side effects and varying responses associated with cancer chemotherapy, as well as the somatic genetic diversity inherent in cancer biology, it is no surprise that oncology has some of the most potential pharmacogenomic applications. Resistance to pharmacologic suppression of EGFR, such as the anti-EGFR monoclonal antibodies panitumumab and cetuximab, is predicted by KRAS mutations. Table 3 shows that KRAS mutations are linked to a worse prognosis (Amado et al., 2008; Lièvre et al., 2006). Maximum patients responding to gefitinib and erlotinib treatment have mutations (somatic) in the EGFR gene. These alterations clustering around the ATP-binding region of the tyrosine kinase domain (exons 18, 19, and 21) are thought to stabilize the drug-tyrosine kinase domain interaction (Table 3) (Kobayashi et al., 2005). In lung cancer, EGFR amplification, which is not as common as previously thought, is also linked to response to cetuximab and gefitinib treatments (Table 3) (Cappuzzo et al., 2005). Imatinib is effective in patients with GIST (gastrointestinal stromal tumor) who have CKIT mutations. In cancers with mutant CKIT or PDGFR, imatinib suppresses cell proliferation. CKIT-activating mutation D816V, on the other hand, has been linked to imatinib resistance (Table 3) (Growney et al., 2005). In breast cancer and other forms of cancer, HER2 gene amplification produces gene overexpression and provides a response to therapy (Cappuzzo et al., 2006; Wong & Lee, 2012). The United States Food and Drug Administration (FDA) permitted trastuzumab for utilization in HER2-positive metastatic breast cancer in 1998, on the basis of a randomized phase III study that found that the trastuzumab and chemotherapy in combination remarkably enhanced objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in previously untreated patients compared to chemotherapy only (Table 3) (Slamon et al., 2001). Despite this considerable result, 70% of patients with HER2-positive breast tumors show intrinsic or secondary trastuzumab resistance, emphasizing the significance of developing novel therapeutics for this disease (Arribas et al.,