UPDATE: Can genetics influence the choice of antihypertensive combination therapy?

Pavel HAMET, OQ, MD, PhD,

Johanne TREMBLAY, PhD,
Centre de Recherche du Centre Hospitalier de
l’Université de Montréal
Quebec, CANADA

Can genetics influence the choice of antihypertensive combination therapy?

by P. Hamet and J. Tremblay, Canada

In this early post-genomic era, the field of pharmacogenomics is now progressively entering a phase of clinical utility evaluation. In terms of therapeutics, many genomic markers have been identified that have an impact on drug metabolism, transport, and targets. Companion diagnostics is a rapidly evolving field that exists in parallel to the development of drugs, and will have future applications for personalized health care. When an opposite effect is observed with two different combinations of antihypertensive drugs in a clinical trial, differences can be expected at the level of genomic polymorphisms or transcriptional effects between subsets of study participants. With the increasing availability of effective fixed-dose combinations of antihypertensive drugs, the development of companion diagnostics may offer rational guidance for selection of the most appropriate combinations for long-term use in individuals.

Medicographia. 2013;35:468-472 (see French abstract on page 472)

Cardiovascular death remains the number one cause of mortality and number one health burden in Canada and throughout the world, with hypertension the leading risk factor.1-3 It is thus relevant that hypertension remains one of the most difficult complex diseases to resolve at the causal genetically-driven level, with many other pathological conditions in recent years having had fundamental aspects of their genetics revealed through the use of novel global genomic technologies. The complexity of hypertension is reflected in the fact that only about 30% to 40% of blood pressure variability is genetically determined, with environment playing a major causal role and interactions between genetic and environmental factors also being of great importance. The past debate on the relative importance of nature versus nurture in shaping human development is nowadays understood in terms of gene-environment interactions. Although still not fully accepted, it is now evident that a more relevant question is not which of the two is the most important, but rather how these two determinants of life can—and do—interact. In diseases such as hypertension, it is evident that a better understanding of the mechanisms of the interactions between genetics and a variety of environmental factors is of the utmost importance. These include interactions between stress, sodium intake/other nutritional factors, physical activity, or antihypertensive medications, and specific genetic polymorphisms, copy number variants, and other genetic variants.4 There are several examples in the literature of the impact of a single allelic variant with a pleiotropic effect not restricted to a specific phenotype. One example is the fat mass and obesity–associated gene, FTO. A single nucleotide polymorphism (SNP) in this gene (rs9939609) has been widely described as being associated with obesity and other components of the metabolic syndrome, but recent evidence also points to a specific impact of the environment in the form of high levels of dietary saturated fat, which accentuates the risk of obesity in carriers of a specific allele of this particular SNP.5 In our studies of obesity and hypertension, we have observed that FTO is particularly susceptible to environmental modulation.6 We showed that the association of the FTO gene with blood pressure is only apparent after withdrawal of antihypertensive medication, as the latter overshadows the relationship of the gene with blood pressure without affecting its relationship with obesity.7 It has also been demonstrated that the genetic risk for hypertension, as defined by an aggregate of most of the known risk alleles for obesity discovered in recent genome-wide association studies (GWAS), may be attenuated by as much as 40% by physical exercise.8 Most of the phenotypes associated with cardiometabolic regulation— including hypertension and its cardiovascular outcomes—are polygenic in nature. This has best been demonstrated by a multistage GWAS performed in nearly 200 000 individuals of European descent, which identified 29 previously-known or new SNP variants that influence blood pressure through pathways leading to the development of hypertension and its outcomes, including stroke and coronary heart disease.9 We have shown a similar polygenic contribution in the French-Canadian founder population, which points to synaptic plasticity pathways as forming a crossroads between hypertension, habitual substance use, obesity, and mental and physical stress10 in a paradigm of gene-environment interactions.

We strongly believe that the ingredients required for resolution of the pathophysiology of hypertension include much more detailed phenotyping, lifelong observation of pathogenetic evolution, reduction of disease heterogeneity, and integration of environmental data using ecogenomic11 and epigenomic tools (www.epigenome.org).

Genetic influence on pharmacodynamic and
pharmacokinetic properties of hypertension

In humans, genetic variation is recognized as being an important determinant of drug response variability. Between 20% and 95% of individual variability is genetically based, a result of sequence variations in drug target proteins, drug-metabolizing enzymes, or drug transporters, which can alter drug efficacy, drug side effects, or both. Genetic polymorphisms of proteins involved in drug targeting (ie, pharmacodynamics) and drug metabolism and transport (ie, pharmacokinetics) are the most important causes of individual variability in drug safety and efficacy. Some genetic variations can affect these factors by changing the biological context or environmental sensitivity of the drug response. Examples of genetic polymorphisms of antihypertensive drug targets are the wellknown insertion/deletion (I/D) variants located in intron 16 of the angiotensin-converting enzyme (ACE) gene. McNamara et al demonstrated that the ACE D allele was associated with significantly poorer survival in patients with heart failure and systolic dysfunction.12 The finding of increased left ventricular dimensions in patients with the DD genotype was in accordance with previous reports, and could be explained by presumably greater concentrations of circulating and tissue angiotensin II associated with the DD genotype. In addition, high doses of ACE inhibitors and β-blockers had the greatest impact in patients with the DD variant (P=0.001) and the least impact in those with I/D and II genotypes (P=0.38). The ACE I/D genotype was also associated with the occurrence of cough, a common side effect of ACE inhibitors. In a recent meta-analysis of 11 trials that included 906 cases of ACE inhibitor– related cough and 1175 controls, Li et al confirmed a significant association between the ACE I/D polymorphism and ACE inhibitor–related cough in those studies that involved participants with a mean age of >60 years,13 but not in studies in which the mean age of participants was ≤60 years. This supports the notion that the ACE I/D polymorphism is an agedependent predictor of risk for ACE inhibitor–related cough.

The genetics of drug-metabolizing enzymes (pharmacokinetics) also plays a critical role in inter-individual differences in antihypertensive drug response and adverse drug reactions. The most important class of drug interactions involves the cytochrome P450 microsomal enzyme system.14 The ability to metabolize a drug along a specific pathway of the cytochrome P450 enzyme system can be modulated by genetic polymorphisms. WithCYP450 polymorphisms, individuals may process the medication too rapidly (ultra-rapid metabolizers) rendering it ineffective, or too slowly (poor metabolizers) causing drug concentrations to build up in the blood, potentially causing adverse reactions, or in the case of prodrugs, ineffective activation. The CYP2C9, CYP2C19, and CYP2D6 enzymes are highly polymorphic and together account for about 40% of hepatic human phase I metabolism. The polymorphisms of CYP1A2, CYP2A6, CYP2B6, and CYP2C8 also contribute to inter-individual differences in drug metabolism. CYP2D6 is prob- ably the most extensively studied drug-metabolizing enzyme in humans; its polymorphism has high clinical importance and was the first among the polymorphic CYP450s to be characterized at the molecular level. Today, more than 48 different drug substrates have been identified for this enzyme, and CYP2D6 is responsible for about 25% of the metabolism of known drugs. About 10% of the general population has a slow acting form of this enzyme, and 7% have a super-fast-acting form. In total, 35% are carriers of an abnormally functioning CYP2D6 allele. The pharmacokinetics of β-adrenergic blocking agents such as propranolol are strongly affected by inducers and inhibitors of CYP2D6. Calcium channel blockers are substrates for, and inhibitors of, CYP3A4. Most ACE inhibitors (eg, enalapril, fosinopril, perindopril, quinapril, ramipril, and trandolapril) are prodrugs metabolized in the liver, although captopril and lisinopril are not. Some animal studies indicate that prodrugs may undergo CYP3A4-dependent biotransformation. However, ACE inhibitors are not involved in significant cytochrome P450–mediated interactions with other drugs. The angiotensin receptor blockers losartan and irbesartan seem to be primarily metabolized by CYP2C9, but hydrochlorothiazide (HCTZ) and chemically-related diuretics are not metabolized by CYP450s, but rather are eliminated by the kidney.15

A growing number of drugs have companion diagnostics, and more than a dozen marketed medications propose or recommend genetic testing for optimal treatment. These examples of the successful use of pharmacogenetic testing, unraveling the basis for certain individual drug responses caused by single- gene polymorphisms, can guide future pharmacogenomics research and its application. Realistically, pharmacogenomics will require complex polygenic and gene-environment considerations in order to prove its clinical utility.

The final response to a drug is determined not only by the aforementioned polymorphisms in genes involved in its metabolism, transport, and target, but also importantly in its gene function, ie, transcriptomic consequences. These, in turn, are the final reflection of complex regulation at the gene expression level, which is subject to intergenic DNA sequence influences, such as those of noncoding RNA. Research into gene pathways and networks, the integration of genetics and genomics, proteomics, metabolomics and epigenetics, noncoding RNA derived from hypothesis-free investigation through GWAS, and prospective clinical trials evaluating the utility and cost-effectiveness of genetic testing in drug therapy are a few examples of areas of exploration that must continue.

Pharmacological target polymorphisms that can modify treatment efficacy

Drug/test combinations (Rx-Dx) using companion diagnostics have the potential to provide many clinical benefits to patients, including identification of potential responders to a specific drug, identification of individuals at risk of adverse events, or use as an adjunct tool for monitoring responses to drugs. A new drug and its companion diagnostics should be developed in parallel, with a cross-reference in the labeling information of both products. The US Food and Drug Administration has recently decided to accelerate the approval of drugs accompanied by companion diagnostics.16 In cancer treatment, it is now possible to prioritize small therapeutic molecules by integrating various omics databases.17

Rationalized drug selection is progressing in such difficult areas as hepatocellular carcinoma, where combination of whole-tumor genomic and transcriptomic data with epigenomic analysis is leading to improved tumor classification and drug selection.18 Similarly, in renal cell carcinoma, assessment of resistance to sorafenib is based not only on genomic polymorphisms, but also its reversibility as detected in studies of tumor transcriptome.19

In the field of hypertension, as mentioned, an international consortium of blood pressure GWAS identified 29 SNPs associated with hypertension, which also predicted heart failure, stroke, and ischemic heart disease.9 Marques et al published a meta-analysis of 74 available microarray experiments integrating genome-transcriptome approaches to identifying genes exhibiting altered expression in the kidney, adrenal gland, heart, and artery of spontaneously hypertensive rats and Lyon hypertensive rats compared with normotensive controls.20 When possible, they separately analyzed the results obtained in young animals (less than 6 weeks of age) from the results obtained in adult rats to differentiate between the onset and maintenance phases of hypertension. While both phases may share common pathophysiological mechanisms, they suggested that different gene sets are responsible for the development and maintenance of hypertension. In another study, transcriptional analysis was performed in spontaneously hypertensive rats with left ventricular hypertrophy treated with different classes of antihypertensive agents, which confirmed the importance of cell growth/proliferation, signal transduction, development, and muscle contraction/cytoskeleton functional groups.21 Although similar genes were affected by the use of different antihypertensives during the course of reduction of left ventricular hypertrophy, therapy with: (i) quinapril; (ii) doxazosin and quinapril combination; and (iii) losartan showed distinct patterns of gene expression. We can expect that when biological differences are noted between two drugs, their functional impact at the gene level will also be different.

Thus, for instance, the effects of HCTZ on blood pressure and metabolism are different from those of indapamide. We found that when indapamide or HCTZ were given to patients for a period of 6 months, different proliferative activities were seen in platelet extracts from these patients, depending on which drug they had been given. Indapamide treatment resulted in much lower proliferative activity, despite HCTZ causing a more pronounced blood pressure reduction, actually similar to that of an ACE inhibitor.22 As HCTZ is the most frequently-used antihypertensive drug in combination therapy, but combination therapy with indapamide has been shown to be effective in lowering total mortality, we recently compared the transcriptomic profile of kidneys from spontaneously hypertensive rats treated with indapamide or HCTZ. Our preliminary results showed that while the expression of 218 genes was modulated in the same direction by both drugs,23 645 and 593 genes were differentially expressed with use of HCTZ and indapamide, respectively. Our data indicate that the differences observed between the two drugs could reside in their differential capacity to induce hypotensive and proliferative genes. It is important to underline here that this type of study is feasible in humans: genomic DNA (for SNPs, copy number variations, epigenetics) is readily obtained from circulating nuclear cells from whole blood or from lymphocytes. Moreover, mRNA and DNA can now be obtained from the same sample of blood using RNAase inhibitors and specialized tubes for relatively easy separation of DNA and RNA. Importantly, RNA extracted from blood has proven useful in the study of the expression of many genes, including those of drug metabolizing enzymes.24

The therapeutic orientation in hypertension is rapidly changing from single drug therapy to fixed-drug combinations, even as initial therapy, as combinations are more effective in reaching therapeutic targets and are sometimes subject to fewer side effects through combination of complementary pathways. Since “hard” clinical outcome studies are currently a prerequisite for uptake of a drug by physicians, the decline observed in total mortality with use of the combination of amlodipine and perindopril versus atenolol and HCTZ in the ASCOT trial (Anglo-Scandinavian Cardiac Outcomes Trial) was seminal in supporting the amlodipine–perindopril combination;ADVANCE (Action in Diabetes and VAscular disease: Preterax and DiamicroN MR Controlled Evaluation) was similarly seminal for support of the combination of perindopril with indapamide versus “usual therapy.” ACCOMPLISH (Avoiding Cardiovascular events in COMbination therapy in Patients LIving with Systolic Hypertension) compared the combinations of amlodipine and benazepril with amlodipine and HCTZ. The calcium channel blocker and ACE inhibitor combination demonstrated a significant superiority on the composite outcome of death and cardiovascular events (primary end point),25 and renal events, as revealed by estimated glomerular filtration rate (eGFR) levels.26 However, the biological difference between these two combinations was revealed through a subanalysis of diabetic individuals in ACCOMPLISH.27 While the ACE inhibitor– calcium channel blocker combination was superior to the ACE inhibitor–HCTZ combination in lowering eGFR levels (–2.2% versus -9.9%), it actually led to an increase in microalbuminuria of 92.2 mg/g. By contrast, the ACE inhibitor– HCTZ combination produced a decrease of 20.1 mg/g in microalbuminuria levels at the end of the study. Our interpretation of these results is that a subset of diabetic individuals may exist in whom the combination of an ACE inhibitor with a calcium channel blocker is beneficial for many outcomes, while in another subset of diabetic indviduals, this combination can clearly be detrimental to renal function, as reflected by the increase in micoalbuminuria levels. This is particularly relevant, as microalbuminuria is a predictor of outcomes in the general population and in both type 1 and type 2 diabetics.28-30

Figure. Proposed framework for the future development of personalized

Abbreviations: ACE, angiotensin-converting enzyme; CCB, calcium channel

We therefore propose the joint use of biomarkers and genetic markers that have demonstrated predictive power for cardiovascular outcomes31 in the identification of individuals who will better respond to specific drug combinations. This should be carried out within the framework for the future development of personalized medicine outlined in the scheme illustrated in the Figure. ■

1. Ezzati M, Lopez AD, Rodgers A, Vander HS, Murray CJ. Selected major risk factors and global and regional burden of disease. Lancet. 2002;360:1347-1360.
2. Joffres MR, Hamet P, MacLean DR, L’Italien GJ, Fodor G. Distribution of blood pressure and hypertension in Canada and the United States. Am J Hypertens. 2001;14:1099-1105.
3. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3:e442.
4. Pausova Z, Tremblay J, Hamet P. Gene-environment interactions in hypertension. Curr Hypertens Rep. 1999;1:42-50.
5. Phillips CM, Kesse-Guyot E, McManus R, et al. High dietary saturated fat intake accentuates obesity risk associated with the fat mass and obesity-associated gene in adults. J Nutr. 2012;142:824-831.
6. Pausova Z, Syme C, Abrahamowicz M, et al. A common variant of the FTO gene is associated with not only increased adiposity but also elevated blood pressure in French Canadians. Circ Cardiovasc Genet. 2009;2:260-269.
7. Noël A, Seda O, Tremblay J, et al. The impact of pharmacological environment on capacity to identify the genetic components of hypertension. Presented at: 23rd International Society of Hypertension Congress; 26-30 September, 2010; Vancouver, Canada.
8. Li S, Zhao JH, Luan J, et al. Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med. 2010;7:e1000332.
9. Ehret GB, Munroe PB, Rice KM, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103- 109.
10. Nikpay M, Seda O, Tremblay J, et al. Genetic mapping of habitual substance use, obesity-related traits, responses to mental and physical stress, and heart rate and blood pressure measurements reveals shared genes that are overrepresented in the neural synapse. Hypertens Res. 2012;35:585-591.
11. Dumas P, Kren V, Krenova D, Pravenec M, Hamet P, Tremblay J. Identification and chromosomal localization of ecogenetic components of electrolyte excretion. J Hypertens. 2002;20:209-217.
12. McNamara DM, Holubkov R, Postava L, et al. Pharmacogenetic interactions between angiotensin-converting enzyme inhibitor therapy and the angiotensin- converting enzyme deletion polymorphism in patients with congestive heart failure. J Am Coll Cardiol.2004;44:2019-2026.
13. Li YF, Zhu XM, Liu F, et al. Angiotensin-converting enzyme (ACE) gene insertion/ deletion polymorphism and ACE inhibitor-related cough: a meta-analysis. PLoS One. 2012;7:e37396.
14. Ingelman-Sundberg M.Genetic polymorphismsof cytochrome P4502D6 (CYP2- D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J. 2005;5:6-13.
15. Flockhart DA, Tanus-Santos JE. Implications of cytochrome P450 interactions when prescribing medication for hypertension. Arch Intern Med. 2002;162:405- 412.
16. Schmidt C. Challenges ahead for companion diagnostics. J Natl Cancer Inst. 2012;104:14-15.
17. Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011;39:D1035-D1041.
18. Marquardt JU, Galle PR, Teufel A. Molecular diagnosis and therapy of hepatocellular carcinoma (HCC): an emerging field for advanced technologies. J Hepatol. 2012;56:267-275.
19. Zhang L, Bhasin M, Schor-Bardach R, et al. Resistance of renal cell carcinoma to sorafenib is mediated by potentially reversible gene expression. PLoS One. 2011;6:e19144.
20. Marques FZ, Campain AE, Yang YH, Morris BJ. Meta-analysis of genome-wide gene expression differences in onset and maintenance phases of genetic hypertension. Hypertension. 2010;56:319-324.
21. Gallego-Delgado J, Connolly SB, Lázaro A, et al. Transcriptome of hypertension- induced left ventricular hypertrophy and its regression by antihypertensive therapies. Hypertens Res. 2009;32:347-357.
22. Hadrava V, Kruppa U, Russo RC, Lacourciere Y, Tremblay J, Hamet P. Vascular smooth muscle cell proliferation and its therapeutic modulation in hypertension. Am Heart J. 1991;122:1198-1203.
23. Dumas P, Corbeil G, Godefroid G, Tremblay J, Hamet P. Differential transcriptomics of indapamide and hydrochlorothiazide in SHR kidney. Poster presented at: 24th Meeting of the International Society of Hypertension; September 30-October 4, 2012; Sydney, Australia.
24. Siest G, Jeannesson E, Marteau JB, et al. Transcription factor and drug-metabolizing enzyme gene expression in lymphocytes from healthy human subjects. Drug Metab Dispos. 2008;36:182-189.
25. Jamerson K, Weber MA, Bakris GL, et al. Benazepril plus amlodipine or hydrochlorothiazide for hypertension in high-risk patients. N Engl J Med. 2008;359: 2417-2428.
26. Bakris GL, Sarafidis PA, Weir MR, et al. Renal outcomes with different fixeddose combination therapies in patients with hypertension at high risk for cardiovascular events (ACCOMPLISH): a prespecified secondary analysis of a randomised controlled trial. Lancet. 2010;375:1173-1181.
27. Weber MA, Bakris GL, Jamerson K, et al. Cardiovascular events during differing hypertension therapies in patients with diabetes. J Am Coll Cardiol. 2010; 56:77-85.
28. de Boer IH, Sun W, Cleary PA, et al. Intensive diabetes therapy and glomerular filtration rate in type 1 diabetes. N Engl J Med. 2011;365:2366-2376.
29. Ninomiya T, Perkovic V, de Galan BE, et al. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol. 2009;20:1813-1821.
30. Smink PA, Lambers Heerspink HJ, Gansevoort RT, et al. Albuminuria, estimated GFR, traditional risk factors, and incident cardiovascular disease: the PREVEND (Prevention of Renal and Vascular Endstage Disease) study. Am J Kidney Dis. 2012;60:804-811.
31. Wong MG, Perkovic V, Woodward M, et al. Circulating bone morphogenetic protein-7 and transforming growth factor-beta 1 are better predictors of renal end points in patients with type 2 diabetes mellitus. Kidney Int. 2013;83: 278-284.

Keywords: companion diagnostics; gene-environment interaction; genetic polymorphism; hypertension; pharmacogenomics