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Themed Issue papers |
1 UF Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA2 University of Illinois at Chicago, Chicago, IL, USA
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(Received 18 November 2004;
accepted after revision 1 February 2005; first published online 18 March 2005)
Corresponding author J. A. Johnson: UF Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA. Email: johnson{at}cop.ufl.edu
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Introduction |
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There are two broad manners in which pharmacogenomics can add to our knowledge of pharmacological agents for cardiovascular disease. The first is to use genomic information to help identify potential new drug targets. The concept is that if the genetic or genomic basis of a disease is understood, then it might be possible to develop highly targeted therapies. Currently, all drugs used today represent only about 500 different drug targets, although it is estimated that there are probably 5000–10 000 potential drug targets in the body. Thus, such an approach clearly has the potential to increase the number of novel drug targets. While there are certain examples in the area of cancer of marketed drugs that have been developed through such an approach, no marketed cardiovascular drugs have been discovered by this strategy. While this is a promising strategy for development of cardiovascular drugs, it will not be the focus of this review.
The second manner in which pharmacogenomics can provide important insights into cardiovascular drug therapy is through elucidation of the genetic (or genomic) contribution to variable response for existing drugs, either those on the market or those currently under development in man.
Cardiovascular pharmacogenomics: current evidence
There are a number of studies in the published literature that provide proof of concept that genetic variation contributes to the variable response that is observed upon administration of cardiovascular drugs. Several interesting examples are discussed here, with additional examples shown in Table 1. These examples are separated based on genetic polymorphisms in drug-metabolizing enzymes or drug targets.
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In the case of cardiovascular drugs, there are some that are substrates for the enzymes that exhibit genetic variability, but in a number of cases, these do not have important clinical consequences. For example, approximately 70–80% of the metabolism of the ß-blocker metoprolol is controlled by the polymorphic cytochrome P450 2D6 (CYP2D6) enzyme, and patients with inactivating mutations on both alleles have no functional protein present. In these patients, called poor metabolizers, metoprolol plasma concentrations can be more than 5 times normal (Lennard et al. 1982, 1983; McGourty et al. 1985). Yet, despite these differences in plasma concentrations, our laboratory has recently shown that neither drug plasma concentration nor CYP2D6 genotype are important determinants of tolerability/adverse effects to metoprolol in patients with either hypertension or heart failure (Zineh et al. 2004; Terra et al. 2005). Many other ß-blockers are also metabolized by CYP2D6, but less extensively than metoprolol. Thus, we conclude that while CYP2D6 polymorphisms have a significant effect on plasma drug concentrations, they do not have an important influence on tolerability of or adverse effects caused by ß-blockers.
The ß-blocker and CYP2D6 example is somewhat typical for the cardiovascular drugs, since it relates to the clinical impact of genetic polymorphisms in the drug-metabolizing enzymes. There are, however, some interesting exceptions to this rule. Perhaps the most striking is warfarin, an anticoagulant drug with a very narrow therapeutic range, and whose metabolism is governed largely by the polymorphic CYP2C9 enzyme. CYP2C9 contain several single nucleotide polymorphisms (SNP) that change the encoded amino acid (called *2 and *3), leading to different functional capacities for metabolism. Up to 40% of Caucasians (fewer Asians and those of African descent) carry at least one variant allele, and both variant alleles have significantly reduced catalytic activity (Daly & King, 2003). Importantly, the presence of one or more variant alleles has been associated in numerous studies with substantially lower warfarin dose requirements, prolongation of the time to stable dosing, and increased risk of bleeding, including serious bleeding episodes (Aithal et al. 1999; Freeman et al. 2000; Higashi et al. 2002; Daly & King, 2003). Given that warfarin requires intensive monitoring and follow up, the potential benefits of using genetic information to help determine the most appropriate dose for a specific patient are clear. On-going studies to document the benefit of a priori use of genetic information to determine warfarin dosage may help to translate this to the clinical setting.
Genetic influences on drug targets. Genetic variability in the proteins involved in drug pharmacodynamics is likely to be ultimately more informative than drug metabolism polymorphisms for the understanding of response variability to cardiovascular drugs. Table 1 provides some examples from the literature, and the reader is referred to several review articles for more detailed information on the cardiovascular pharmacogenomics literature (Johnson & Humma, 2002; Terra & Johnson, 2002; Roden, 2003). Herein, we provide selected examples from the cardiovascular drug target pharmacogenomics literature.
The therapeutics areas with the most literature in cardiovascular pharmacogenomics are hypertension, lipid disorders and heart failure. In the area of hypertension, there are data showing genetic associations between drug targets and antihypertensive response for diuretics, ß-blockers, ACE inhibitors and angiotensin1 (AT1) receptor blockers. The only first line drug class with no data is the calcium channel blockers, and this is because there are limited data available on sequence variability in genes relevant to the calcium channel blocker response. Work on calcium channel blocker pharmacogenomics is on-going in our laboratory, so data will be forthcoming in this area.
In the case of ß-blockers, several different studies have shown an association between polymorphisms in the ß1-adrenergic receptor gene and blood pressure lowering (Johnson et al. 2003; Liu et al. 2003; Sofowora et al. 2003). One group has also found an association between a G protein 
s subunit polymorphism and ß-blocker response (Jia et al. 1999). This association is also consistent with pharmacology of the ß-blockers, since the ß-receptors couple to Gs.
For the diuretics, a number of different studies have shown genetic associations between blood pressure response and polymorphisms in the G protein ß3 gene and the -adducin gene (Cusi et al. 1997; Glorioso et al. 1999; Turner et al. 2001; Psaty et al. 2002; Sciarrone et al. 2003). In the case of the -adducin polymorphism, a non-synonymous Arg460Trp SNP has been associated with increased renal tubular sodium reabsorption and differential diuretic response (Cusi et al. 1997; Manunta et al. 1998; Glorioso et al. 1999). Consistent with the data on ß-blocker pharmacogenetics in hypertension, data are emerging on the genetic determinants of response to ß-blockers in heart failure. Three independent studies, including our own and work from the laboratory of Liggett and colleagues, have shown that the Arg389Arg genotype of the ß1-adrenergic receptor is associated with the greatest response (defined as either improvement in left ventricular function or survival) to ß-blocker therapy (Mialet Perez et al. 2003; Terra et al. 2004).
Genetic associations with responses to cholesterol-lowering therapies, particularly statins, have also been extensively investigated (Ordovas et al. 1995; Jukema et al. 1996; de Maat et al. 1998, 1999; Kuivenhoven et al. 1998; Heath et al. 1999; Gerdes et al. 2000; Boekholdt et al. 2003; Dornbrook-Lavender & Pieper, 2003; Schmitz & Drobnik, 2003; Kajinami et al. 2004) and have been summarized (Dornbrook-Lavender & Pieper, 2003; Schmitz & Drobnik, 2003). Genes for which positive associations have been shown include those for apolipoprotein E, stromelysin-1, CETP, ß-fibrinogen, LDL receptor, lipoprotein lipase, toll-like receptor 4 and cytochrome P4503A4, among others. These studies have evaluated a number of different drug response phenotypes, including lipid lowering, atherosclerosis regression and cardiovascular outcomes. In the final category is a study from one of the first statin survival trials, 4S, which showed that carriers of the Apo
4 allele derived the greatest risk reduction from simvastatin therapy (Gerdes et al. 2000).
There is also interest in defining the genetic determinants for serious toxicities that are cardiovascular in nature and/or are caused by cardiovascular drugs. Perhaps most widely studied in this group are the genetic determinants of drug-induced QT prolongation and Torsades de Pointe TdP). Numerous studies have found mutations (most have allele frequencies < 0.01) in ion channel genes, particularly the sodium and potassium channels, in patients who have experienced drug-induced QT prolongation or TdP (Drici & Barhanin, 2000; Napolitano et al. 2000; Sesti et al. 2000; Splawski et al. 2002; Yang et al. 2002; Roden, 2003). While these studies provide clues to the genetic basis of risk for drug-induced QT prolongation, the data are far from the goal of using genetic screening to help clinicians identify those patients at risk of drug-induced QT prolongation. As such, this remains an important research question for basic and clinical scientists.
Experimental approach to date: a candidate gene-driven approach
All of the studies highlighted above can be described as ones that followed a candidate gene approach. This means that genes and polymorphisms were selected for study based on suspicion that they might be associated with the drug response of interest.
Pharmacogenomics knowledge pyramid using the candidate gene approach. Many working in the field of pharmacogenomics view the candidate gene approach as building on a knowledge pyramid, as illustrated in Fig. 1. Below, we discuss the various experimental approaches used in the various steps of this pyramid. This figure is adapted from one highlighted on the NIH's Pharmacogenetics and Pharmacogenomics Knowledge Base website (http://www.pharmgkb.org/). It should be noted that this database is being built as the central repository for pharmacogenetic data and findings, and interested researchers are encouraged to access and deposit data into this database.
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In vitro functional studies. Once a genetic polymorphism is identified, there is interest in knowing whether it has functional consequences. Functional studies are commonly conducted with in vitro mutagenesis of cell culture systems or through the use of transgenic animal models. Some investigators argue that functional studies should be done prior to any clinical association studies, while others suggest that functional studies might be reserved until there is evidence of association with a clinical phenotype. However, there is little debate about their importance.
Clinical studies. The next step on the knowledge pyramid is to build evidence that there is a clinical association with the polymorphism. Occassionally, these clinical studies are first done in normal volunteers (rather than the relevant patient population). In certain situations this is reasonable, whereas in others it might be misleading and could lead investigators to believe that there are no genetic associations with drug response, when an association might be present in patients with disease. Thus, the clinical studies of most importance are those conducted in a relevant patient population, in a manner that reflects the usual treatment practices. Without this, it will not be possible to move the information to the point of clinical utility.
Future approaches
Candidate gene approach driven by polygenic nature of drug response. The current literature provides clear proof of concept that genetic variability contributes to the interpatient variability in drug response. However, most of the studies have focused on a single gene (and often on a single polymorphism within the gene) and, while this might provide evidence of a genetic association with response, it usually does not explain enough of the variability to be useful clinically. This concept is discussed in detail elsewhere (Johnson, 2003; Johnson & Lima, 2003). Movement towards a more extensive candidate gene approach or a genomic approach is probably necessary, in order to reach the next step of the pyramid (i.e. explaining a sufficient degree of response variability for genetic information to be useful in the clinical setting).
A broader candidate gene approach includes genes that contribute to both the drug's pharmacokinetics and its pharmacodynamics. Thus, candidate gene approaches that consider 50–100, or more, candidate genes, based on their involvement in the drug pharmacokinetics, pharmacological action or the disease state, is an approach that is increasingly employed.
Genomic approaches. While much has been learned through candidate gene-driven studies, there are certain limitations to this approach, the greatest of which is that gene selection is based on our present understanding of the mechanism of action of the drug, its pharmacokinetics and the disease state. Thus, the candidate gene approach precludes identification of novel genes that might contribute to variable drug response. Thus, one advantage of a genomic approach is that it requires no previous knowledge of gene function, proteins involved in disease biology or drug effects. Since a genome-wide approach has the potential to identify unsuspected genetic associations with drug response, it may provide insight into previously unknown disease mechanisms, drug effects and/or targets for disease intervention.
A genomic approach commonly used in animal models and certain human diseases is gene expression microarray studies, which requires collection of a tissue of interest and testing for differential expression levels of mRNA. This approach can be very informative and provide new insights into disease or drug response. However, in the setting of human studies of cardiovascular disease, this approach has certain limitations. First, access to tissues of interest in cardiovascular disease represents a significant barrier to these approaches in human studies. But, perhaps more importantly, many of the cardiovascular diseases are essentially systemic diseases, e.g. hypertension, heart failure and lipid disorders. In these diseases, the most appropriate tissue for study would not be clear. For example, in hypertension, the response to antihypertensive drugs may be based on a combination of their effects on the brain, heart, kidney and vasculature. Given the difficulties in accessing relevant human tissue and uncertainties about the most relevant tissue for study, RNA microarray studies may have limited utility in human pharmacogenetic studies of cardiovascular drugs.
Another approach is the genome scanning approach, which tests for association with a phenotype with microsatellite markers spread throughout the genome. This allows for localization on the chromosome where causative genes for the disease of interest may lie. This approach is common in cardiovascular disease studies, and there have been numerous recent publications using such an approach to decipher the genetic contribution to hypertension (Caulfield et al. 2003; Samani, 2003). However, this approach is usually not feasible within the context of pharmacogenomics studies, because these are family-based studies that use large pedigrees or discordant siblings to test for the association. While family studies are common in genetics research, they are not usually feasible within pharmacogenomics because it is rare that multiple family members, particularly across generations, have exposure to the same drug and have had their response to that drug carefully characterized.
For the reasons described above, several of the common genomic approaches used to aid in understanding of disease are not particularly amenable to cardiovascular pharmacogenomics research. Genomic approaches that focus on genomic DNA and variability within the genome are therefore the approaches that have the greatest potential application in cardiovascular pharmacogenomics (Kennedy et al. 2003; Zhong et al. 2003). To date, published data using such approaches for cardiovascular drugs are limited, although such studies are clearly underway.
Approaches utilizing SNP maps across the genome are likely to be the most commonly used genomic approaches within cardiovascular pharmacogenomics. The objective under this approach is to use SNPs from across the genome to identify those chromosomal locations associated with drug response variability. SNP maps available commercially at present can test over 100 000 SNPs using chip technology (e.g. the Gene Chip® Mapping 100K Array from Affymetrix; http://www.affymetrix.com/products/arrays/specific/100 kaffx; Kennedy et al. 2003). Given that there are an estimated ten million SNPs, this approach captures only a small portion of the genetic variability, although it is perhaps the best available at this time. Eventually this approach will be perfected further through data generated through the International HapMap Project (http://www.genome.gov/10001688). The goal of the HapMap project is to determine the SNPs in the haplotype that uniquely define that haplotype, such that only a small portion of the genetic variability in that block would actually have to be tested. It is anticipated that the HapMap project might identify about 500 000 tag SNPs that could be genotyped to represent the genetic diversity across the entire human genome.
An alternative approach focuses on the functional polymorphisms across the genome, namely non-synonymous SNPs, premature stop codons or other SNPs causing radical effects on the protein, and regulatory SNPs. This approach, performed using Taqman genotyping methods from Applied Biosystems (http://myscience.appliedbiosystems.com/navigation/mysciApplications.jsp?tabNameAttribute=applSp), tests for approximately 30 000 putative functional SNPs across the genome and is based on the fact that the vast majority of genetic variation with functional consequences comes from these types of polymorphisms (Botstein & Risch, 2003).
The genomic approaches currently available are expensive, but not prohibitively so. Over the next 5–10 years, as the costs of genotyping fall further and the technologies for genome-wide SNP genotyping are enhanced, it is anticipated that these changes will increase utilization of such approaches. The greater challenges are likely to lie in the interpretation of the genomic variability data and the statistical genetics approaches needed to analyse these data.
Conclusion
Pharmacological management of cardiovascular diseases is largely based on evidence from clinical trials and guidelines from expert consensus panels. This results in treatment recommendations for the population, with recognition that not all individuals will derive the benefit seen in large samples. Pharmacogenomics holds the promise of allowing further refinement in the management of patients, by enabling clinicians to individualize cardiovascular drug therapy based on a person's genetic make up. Such an approach has the potential to lead to greater efficacy in treated patients, fewer adverse effects and better disease management, which might in turn lead to a reduction in cardiovascular events (i.e. better outcomes) and reduced health care costs. This goal is still probably at least a decade from wide adoption of even a limited number of examples into clinical practice. However, it seems likely that genetic data will be an important tool used in guiding drug therapy in the next generation.
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