Abstract
Patients’ responses to drug therapy vary widely. Pharmacogenomics, as a novel field, elucidates the genetic factors that underlie the observed differences in drug metabolism. It is only through pharmacogenomic research that the genetic differences that affect the pharmacodynamic and pharmacokinetic properties of drugs can be understood. Research shows that about 20 percent of drug metabolism phenotypes can be attributed to genetic factors (Deverka, 2009).
In particular, genetic polymorphisms involving certain genes that encode for drug-metabolizing enzymes have a major effect on drug metabolism. Pharmacogenomics promises to improve drug efficacy and safety by increasing our understanding of how genetic factors affect drug metabolism. This could potentially result in safe and effective personalized medicine with significant implications for public health. This paper examines the topic Pharmacogenomics and its application in clinical research, pharmacology, and public health improvement.
Reasons for the Current Importance of the Topic
Vast individual differences exist with regard to drug response, which determines drug efficacy and/or host toxicity. Studies have established that the differences in drug response depend on factors such as age, drug interactions, race/ethnicity, and nature of the disease, among others. While these factors influence drug metabolism in patients, genetic polymorphisms involving drug receptors play a pivotal role in determining the differences in treatment efficacy and toxicity among individuals. Genetic variations alter a drug’s properties, which, in turn, affect its metabolism.
Inherited variability in drug metabolism documented in many clinical studies has given rise to Pharmacogenomics. Pharmacogenomics, as a field, examines how genetic variations among humans influence their responses to medicines. Therefore, Pharmacogenomics helps unravel the role of genetic differences in drug metabolism, which is an important step in the development of personalized medicine. In recent years, advanced genetic tools for identification of gene variants have been developed. Moreover, the existing Bioinformatics tools and the human genome projects have generated an enormous amount of genomic data, which help researchers elucidate the relationship between genes and drug therapy.
Since the discovery of the role of genetic polymorphism in drug metabolism in the 1980s, the genes that encode for drug-metabolizing enzymes, including CYP2D6, have been cloned in vitro. Using advanced molecular tools, researchers have been able to identify, isolate, and clone genes coding for specific pharmacogenetic traits in order to elucidate their role in drug metabolism. Typically, the genes that code for enzymes involved in drug metabolism are ‘monogenic’. They are important in understanding the pharmacological effects of drugs because they code for polymorphic enzymes that activate or repress drug receptors and substrates in the human body.
Polymorphisms generate ‘isoenzymes’, which suppress the potency of drugs such as fluorouracil. On the other hand, the inactivation of drugs with a large therapeutic index has no much impact on their therapeutic effects. Thus, it can be concluded that people’s genotypes affect how they metabolize drugs. Clinical trials cannot effectively identify the polygenic traits and/or pathways behind the observed differences in drug metabolism. It is even more difficult when the mechanism of action of the candidate drug is not well understood. In this regard, Pharmacogenomics helps elucidate the molecular mechanism and gene variants that define the inherited differences in drug metabolism in order to understand the pharmacological effects of drugs.
Once the mechanism of action of a drug becomes clear, its toxicity can be minimized and its efficacy enhanced to achieve a desired therapeutic effect on a particular patient. Moreover, high-throughput screening techniques have been developed to help in the detection of specific genes that predispose individuals to certain diseases that require individualized treatment.
Thus, Pharmacogenomics is important because it helps in the identification of genes and gene networks that influence drug efficacy and toxicity. Through phramacogenomics drug targets can be identified and used to optimize the therapeutic effects of a medication. Moreover, Pharmacogenomics makes it possible to develop individualized medicine based on a person’s natural (genetic) ability to utilize a particular drug and his/her responses to the treatment therapy.
Approaches Used to Examine the Topic
The genetic differences in the way people respond to drugs were discovered through clinical studies. Vast differences between individuals were identified with regard to their response to drugs such as debrisoquin. Patients using debrisoquin showed marked differences in blood pressure with some displaying the symptoms of hypotension (Cascorbi, Paul & Kroemer, 2004). Clinical observation is one approach used to detect interpersonal differences in drug metabolism. It entails population studies, biochemical techniques, and molecular tools that detect gene variants (clinical phenotypes) that have significant clinical effects on drug metabolism.
Recent advances in gene (DNA) sequencing have revolutionized Pharmacogenomics. Modern molecular tools detect polymorphisms, which are then assessed in clinical studies to determine their effects in the patients. The clinical trials help elucidate the enzyme variants that confer individuals with differing drug metabolizing ability, which explains the interpersonal differences in drug response (Deverka, 2009). The techniques used in Pharmacogenomics can be classified into two main groups: the pre-genomic and post-genomic approaches. The pre-genomic approaches, which were popular before the 21st century, involved the identification of uncommon drug effects (phenotype) in patients under therapy. A series of clinical studies (retrospective) were then carried out to identify the inheritance mechanisms and patterns. The specific gene was isolated and cloned to validate the identity of the phenotypes.
The second group, the post-genomics techniques, became popular after the year 2000. They involve high-throughput molecular techniques such as DNA sequencing and microarrays that detect single nucleotide polymorphisms (SNPs) in an individual’s genome. They utilize human genome project data stored in large databases to analyze the relationships between drug metabolism (phenotypes) and specific gene variants (Deverka, 2009). These approaches unravel the mechanisms involved in drug metabolism in each observed phenotype, the therapeutic effects of the medication, the enzymes involved in drug metabolism, and the drug’s therapeutic index (Deverka, 2009). These factors have a big influence on an individual’s response to a specific drug.
The selection of the candidate gene(s) associated with a particular therapeutic phenotype involves different strategies. First, the gene must be associated with the therapeutic phenotype being examined. The candidate gene (enzyme) must also play a role in a known pharmacological pathway. To investigate the relevance of genetics in the pharmacokinetics of a medication, the primary target often involves genes known to play a role in “drug absorption, distribution, and biotransformation” (Deverka, 2009, p. 150). Such a study will also investigate the excretion pathways of the metabolites derived from the drug. On the other hand, when investigating the relationship between genes and the pharmacodynamic characteristics of a drug, the drug-gene interaction (receptor molecule) becomes the primary target. Other potential targets would include known enzyme inhibitors and genes involved in signaling pathways.
After a candidate gene that is relevant to a desired therapeutic phenotype has been selected, the next step involves the identification of single nucleotide polymorphisms (SNPs) associated with the gene. The genetic polymorphisms to be studied are selected based on past research findings. In addition, public databases that contain human gene polymorphisms such as the 1000 Genomes project can provide information about SNPs relevant to the selected candidate gene (Hansen, Brunak & Altman, 2009).
Depending on the aim of the study, researchers can target different SNPs available in the public resources. The first method, tag-SNPs, involves the use of specific software to detect all the genetic variants (SNPs) associated with the candidate gene. Researchers can also seek for expression quantitative trait loci (eQTLs), which are closely related to certain therapeutic phenotypes such as drug susceptibility and cytotoxicity (Hansen, Brunak & Altman, 2009). Another key consideration when selecting the polymorphisms to investigate is the ancestry of the population. Genetically diverse individuals often require more variants to capture the entire polymorphisms in the population.
Besides the candidate gene method, researchers use a genome-wide analysis method to detect SNPs. Genome-wide approach involves the use of micro-arrays (gene chips) to detect polymorphisms through genotyping or gene expression analysis (Hansen, Brunak & Altman, 2009). Researchers use microarrays to profile the mutations and SNPs. The data generated is then used for pharmacogenomic discovery. Microarrays generate large amounts of data sets. Thus, data analysis and processing require advanced Bioinformatics tools such as Bioconductor and R-software. While the candidate gene method focuses on a single gene or SNP, the genome-wide approach examines the complete genome associated with a particular therapeutic trait. Therefore, the genome-wide method requires advanced statistical approaches to analyze and link the data to a particular therapeutic phenotype.
Pharmacogenomic research depends on the development of tools in areas such as Bioinformatics and genotyping. It also relies on the human genome data available in various databases and resources. Genetic variants (SNPs) form the foundation of modern pharmacogenomic research because they genetic variation has a huge influence on drug metabolism. For instance, the relationships among gene variants, drug toxicity, and genetic phenotypes have been associated with anticancer agents in humans (Hansen, Brunak & Altman, 2009). Thus, the genome data (SNPs) stored in databases can be used to identify the relationship between gene variation and drug metabolism. Pharmacogenomic research has a potential of improving the efficacy of treatments and reducing cytotoxicity through individualized medicine, which will result in optimized therapeutic effects on patients.
Results of the Studies
Each person metabolizes a particular drug differently from other people. Genetic variability has a significant influence on people’s responses to drugs. Numerous studies on biomedical research have been published on major databases covering a broad range of topics on genes, drugs, illnesses, and the relationships among them. In this report, seven research articles (after 2000) documenting the findings of studies on drug-gene relationships were selected from major databases (Pubmed). The studies focus on the pharmacogenomic aspects of common illnesses, including cardiovascular conditions and asthma.
Hypertension or high blood pressure is a leading cause of heart failure and mortality (Hansen, Brunak & Altman, 2009). Although many pharmacological treatments for this condition exist, less than 33 percent of people in North America have attained the required pressure levels (Hansen, Brunak & Altman, 2009). This has made the management of hypertension a challenge among healthcare professionals. Research has established that differences in mineral (sodium, potassium, and calcium) absorption cause the variation observed between African-Americans and Whites with regard to hypertension (Deverka, 2009).
The differences in the severity of high blood pressure among blacks and whites can be attributed to these candidate genes (Deverka, 2009). However, extrinsic factors such as psychosocial issues have also been found to increase the effects of hypertension among blacks. According to Deverka (2009), to manage hypertension effectively, African-American patients need higher concentrations of angiotensin-converting enzyme (ACE) inhibitors than whites do. ACE inhibitors are effective drugs in the treatment of patients with “congestive heart failure, myocardial infarction, and diabetic nephropathy” (p. 148).
Pharmacogenomics has helped reduce deaths among patients with cardiovascular conditions. As a standard care, patients with cardiovascular conditions are given ACE inhibitors and β -blockers (Guessous, Gwinn, Yu, Yeh, Clyne & Khoury, 2009). However, the therapeutic outcomes vary from one patient to another. According to Guessous et al. (2009), the differences observed in patient response to these drugs are due to genetic polymorphisms involving “β-adrenergic receptors, the rennin-angiotensin-aldosterone system, and the endothelial system”. Microarray data analysis has established that mutations involving the ACE gene create polymorphisms, which are represented by the phenotypes observed among cardiovascular patients. Moreover, deletion polymorphism has been associated with enhanced ACE activity, which increases a person’s risk of heart failure (Guessous et al., 2009). Genetic variability has also been associated with the way patients with cardiovascular complications respond to β-blockers (Guessous et al., 2009).
A study by Goetz, Kamal and Ames (2008) found that the inclusion of hydralazine and isochron (isosorbide dinitrate) in the medications used in the management of hypertension reduces blood pressure among African-American patients with advanced cardiovascular complications. The study also found that insufficient nitric oxide in a patient increases his/her risk of developing heart failure (Goetz, Kamal & Ames, 2008). Thus, genetic variation (polymorphism) observed in heart failure patients can be attributed to insufficient nitric oxide. The study proposed that nitric oxide therapy could help such patients manage heart failure more effectively.
Furthermore, the specific gene that causes a nitric oxide deficiency has not been characterized (Goetz, Kamal & Ames, 2008). Nevertheless, the study underscored the fact that heart failure and related complications are ‘polygenic’. It also found that genetic variants identified in the African-American population have a significant impact on patient outcomes. Coronary heart disease (CHD) also involves multiple polymorphic genes. They include genes associated with “β-fibrinogen, cholesterol ester transfer protein, apolipoprotein E, and stromelysin-1”. These polymorphic genes influence CHD patients’ response to ‘statin’ treatment (Goetz, Kamal & Ames, 2008, p. 163).
Asthma is another condition whose pathophysiology is associated with genetic polymorphism. Pignatti (2004) found a high variability among patients with regard to their response to asthma drugs, including “β2 agonists, corticosteroids, and leukotriene antagonists” (p. 344). The researcher established that 65 to 80 percent of the subjects (patients suffering from asthma) responded positively to these drugs while the rest showed no response at all (p. 342). Beta2 agonists (short acting) like albuterol (administered through inhalation) has been used in the management of chronic asthmatic attacks.
Guessous et al., (2009) show that agonists such as beta2, which are widely used to control asthma, are not effective in some patients. The difference in drug response can be attributed to the modifications in the amino acid sequences that make up the beta2 receptor proteins (Pignatti, 2004). It has been found that individuals with glycine genotype show less symptoms of asthma than those with arginine genotype (Pignatti, 2004). In the same study, more African-Americans (about 20 percent) have the arginine variant compared to whites (17 percent) (p. 347).
Each person responds differently to Leukotriene modifiers. These drugs inhibit the 5-lipoxygenase (five-LO) pathway, which affects the constriction of the bronchi (Hansen, Brunak & Altman, 2009). Very few patients respond positively to LT modifiers or receptor agonists. A genome-wide analysis of the human genome identified multiple polymorphisms in the five-LO loci (Hansen, Brunak & Altman, 2009). Patients with a mutation in this gene locus responded well to zafirlukast treatment compared to those with normal genotype. Another gene, the LTC4 synthetase, has been found to increase a person’s risk of developing asthma (Hansen, Brunak & Altman, 2009).
The gene increases the secretion of cysteinyl leukotrienes, compounds that increase the severity of asthmatic attacks (Hansen, Brunak & Altman, 2009). Patients with the LTC4 gene have been found to respond well to LT modifiers. Genetic variability has also been found to determine a patient’s response to corticosteroids therapy. Gene mutations involving the “corticotrophin-releasing hormone receptor-1 and T-box alleles” have been associated with a positive response to corticosteroids by asthmatic patients (Hansen, Brunak & Altman, 2009, p. 185).
Genotypes also determine a person’s response to drugs such as warfarin. Warfarin has a small therapeutic index, which makes the determination of a correct dose difficult. Overdose can lead to severe bleeding (Limdi & Veenstra, 2008). Usually, the dosage (warfarin) for each patient is grounded on his/her age, gender, and the nature of the condition, among others (Limdi & Veenstra, 2008). However, genetics and environmental factors have a big effect on a person’s response to this drug. Limdi and Veenstra (2008) examined how the variants of the CYP2C9 gene determine the patients’ responses to warfarin therapy. The study found out that polymorphisms involving the CYP2C9 gene are associated with warfarin dosage differences in 10 percent of the patients.
The drug, warfarin, works by suppressing an enzyme that catalyzes the production of clotting factors in the body (Limdi & Veenstra, 2008). Genetic mutations have produced variants of the enzyme, vitamin K epoxide reductase (VKORC1), which confer individuals with varying responsiveness to warfarin (Limdi & Veenstra, 2008). Thus, through Pharmacogenomics, researchers have been able to identify the polymorphisms involving the VKORC1 gene that affects people’s responsiveness to the drug. In this regard, developments in Pharmacogenomics will enable physicians to prescribe correct warfarin dosage for each patient based on how he/she responds to the drug. This will help control the anticoagulant effects of this drug and achieve improved drug efficacy.
Significance of the Results
Pharmacogenomic studies have established the pharmacodynamic and pharmacokinetic variability that exists in populations. Thus, besides environmental factors, genetic variations involving a single gene or a gene network can affect a person’s responsiveness to certain drugs. Drug efficacy and cytotoxicity can be evaluated based on genetic screening of a population category (e.g., blacks, whites, Asians) to determine its drug responsiveness. Besides race, genotyping can be used to detect the genetic variations that affect medication response (Guessous et al., 2009). Thus, pharmacogenomics can be used to determine the correct drug and dosage for a patient. Currently, pharmacogenomic testing for enzymes and genes such as CYP2C9 and VKORC1 exist (Guessous et al., 2009). The tests give clear information about the relationship between genotype and response to drugs such as warfarin and ACE inhibitors.
It is understood that the clinical symptoms (phenotypes) of many diseases vary from one person to another. This indicates that a particular disease or condition is never homogeneous, that is, a disease has many subtypes, as genetic variations act on the same disease differently. Pharmacogenomic studies explore the genetic polymorphisms that affect the manifestation of a particular illness. The studies examine the variations in particular genes or genomic regions, which encode for enzymes or receptors that interact with the drug (Guessous et al., 2009). The results of these studies can help physicians in drug selection and dose determination for particular population groups or individuals. Information about polymorphisms involving specific genes can be found on publicly available resources and databases. Physicians can use this information to prescribe a drug based on a patient’s predicted response to the drug.
Although Pharmacogenomics is a relatively new area, it has many potential benefits in clinical therapy. Pharmacogenomic information will enable physicians to prescribe drugs in a way that enhances efficacy and ensures patient safety. The information can also help doctors to predict whether a patient will negatively react to a particular medication. In this regard, the doctor can give a different drug that does not elicit a negative response in order to improve the patient’s health outcomes. Thus, phamacogenomics will allow physicians to assess a person’s likely response to a drug based on his/her genotype. This will ensure that each patient receives an appropriate medication therapy for his or her condition.
The results of Pharmacogenomic studies have many clinical applications. For example, many genetic variants of the CYP 450 gene exist, which produce different CYP enzymes (Guessous et al., 2009). These isoenzymes are involved in the metabolism of many drugs (over 30 drugs). Using Pharmacogenomics, physicians can give appropriate drugs to each patient based on his/her CYP profile (Guessous et al., 2009). This will help maximize the therapeutic effect of the drugs.
Potential Impacts of Pharmacogenomics on Improving Human Health
Pharmacogenomics has many potential therapeutic applications, particularly in cancer treatment. Its therapeutic benefits relate to treatment efficacy and reduction of adverse drug effects. Clinicians can use Pharmacogenomics in dosage determination to enhance medication effectiveness and drug safety. In this regard, Pharmacogenomics has a potential of improving public health.
With regard to medication safety, warfarin, a drug with a small therapeutic index, is associated with bleeding in about 10 percent of patients (Pignatti, 2004). Pharmacogenomics can be used to screen for the VKORC1 and CYP2C9 gene variants in order to determine individuals who can tolerate warfarin in small doses. In this way, the adverse effects (bleeding) associated with warfarin overdose can be prevented. Thus, Pharmacogenomics can be used to enhance drug safety and public health outcomes. However, ethical and cultural issues surrounding Pharmacogenomics hamper testing in real-world situations.
Besides improving medication safety, Pharmacogenomics has a potential of enhancing treatment efficacy. An example is tamoxifen, a drug used in the treatment of breast cancer in women. For this drug to work effectively, certain metabolic enzymes must first trigger it. Research has established that polymorphic forms of the CYP2D6 gene increase the likelihood of breast cancer development or recurrence (Pignatti, 2004). Patients with a less active gene (CYP2D6) cannot metabolize tamoxifen. Such patients can be given an aromatase inhibitor, which is a good substitute of tamoxifen. Thus, Pharmacogenomics promises to enhance drug efficacy through genetic testing to determine the risks and benefits of a particular drug. This has a potential of improving the public health outcomes by ensuring that patients receive safe and effective therapy.
References
Cascorbi, I., Paul, M. & Kroemer, H. (2004). Pharmacogenomics of heart failure–focus on drug disposition and action. Cardiovascular Research, 64(1), 32–39.
Deverka, P. (2009). Pharmacogenomics, evidence, and the role of payers. Public Health Genomics, 12(5), 149–157.
Goetz, M., Kamal, A. & Ames, M. (2008). Tamoxifen pharmacogenomics: the role of CYP2D6 as a predictor of drug response. Clinical Pharmacology & Therapeutics, 83(3), 160–166.
Guessous, I., Gwinn, M., Yu, W., Yeh, J., Clyne, M. & Khoury, M. (2009). Trends in Pharmacogenomic epidemiology: 2001–2007. Public Health Genomics, 12(1), 142–148.
Hansen, N., Brunak, S. & Altman, R. (2009). Generating genome-scale candidate gene lists for pharmacogenomics. Clinical Pharmacology &Therapeutics, 86(2), 183-189.
Limdi, N. & Veenstra, D. (2008). Warfarin pharmacogenetics. Pharmacotherapy, 28(1), 184–197.
Pignatti, P. (2004). Trends in pharmacogenomics of drugs used in the treatment of asthma. Pharmacology Research, 49(4), 343–349.