Pharmacogenetics in Cardiovascular Antithrombotic Therapy

Marı́n, Francisco; Roldán, Vanessa; Gónzález-Conejero, Rocío; Corral, Javier

doi:10.2174/156801605774322346

Cited by 16 publications

(16 citation statements)

References 89 publications

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“…While genetic information is far from ready for clinical use in CVD prediction, genetics have made important clinical inroads in other areas, such as pharmacogenomics for predicting efficacy and adverse events of common cardiovascular drugs (reviewed in 98). Whether “we will get there” for genetic CVD prediction, as we have asked in the title of our review, remains an open question.…”

Section: Discussionmentioning

confidence: 99%

Genetic Cardiovascular Risk Prediction

Thanassoulis

Vasan

2010

Circulation

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IntroductionMajor advances in genetics, including the sequencing of the human genome in 20011 , 2 and the publication of the HapMap in 2005 3 , have paved the way for a revolution in our understanding of the genetics of complex diseases, including cardiovascular disease (CVD). After years of inconsistent results and failures to replicate putative candidate gene associations, high throughput technologies (that genotype over 500,000 genetic markers, known as single nucleotide polymorphisms [SNPs]) and novel statistical tools have led to a literal explosion of novel genetic markers associated with complex human diseases. In the context of CVD, these advances have been remarkably successful in uncovering many novel genetic associations with myocardial infarction and cardiovascular risk factors such as lipids, blood pressure, diabetes and obesity. A major objective of these studies has always been to provide new insights into the biology of cardiovascular disease. However, a highly touted additional aim of these discoveries has been to use these genetic markers to usher in a new era of personalized medicine by incorporating genetic information into risk prediction (including for the primary prevention of CVD). In fact, direct-to-consumer testing of recently discovered genetic markers has proliferated despite a lack of evidence for clinical use. 4 As with all nascent technologies, many fundamental questions remain to be answered: Can genetic markers or gene scores improve CVD risk prediction, over and above, validated risk algorithms such as the Framingham risk score and a family history of CVD? How many SNPs are responsible for the genetic component of CVD, and how many genetic markers will we need to discover to reliably improve risk prediction? What are the implications of the allelic architecture of CVD and other complex diseases for risk prediction? And, finally, what steps will be needed prior to bringing this information to patients? In this review, we will examine each of these questions with regards to risk prediction of coronary artery disease (CAD) and myocardial infarction (MI) in a primary prevention setting. Cardiovascular Risk Prediction -Is there a need for improving currently used algorithms?For over five decades, the major cardiovascular risk factors, namely male sex, hypertension, cholesterol, smoking and diabetes have been well known.5 Based on these factors, a number of risk prediction algorithms scores have been developed, including the Framingham risk Address Correspondence to: Ramachandran S. Vasan, MD Framingham Heart Study 73 Mt Wayte, #2 Framingham, MA 1703 Tel. 508-935-3450 Fax. 508-626-1262 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the co...

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Section: Discussionmentioning

confidence: 99%

Genetic Cardiovascular Risk Prediction

Thanassoulis

Vasan

2010

Circulation

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“…The CYP2C19 isoenzyme is involved in both metabolic steps. Therefore, genetic polymorphisms associated with reduced enzymatic activity or drugs that interfere with enzyme activity (e.g., proton pump inhibitors), may impair clopidogrel's effects leading to potential complications (82)(83)(84).…”

Section: Clopidogrelmentioning

confidence: 99%

Diabetes and antiplatelet therapy: from bench to bedside

Rios

Franchi

Rollini

et al. 2018

Cardiovasc. Diagn. Ther

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Diabetes mellitus (DM) is a metabolic disorder associated with accelerated atherogenesis and an increased risk of atherothrombotic complications. Multiple mechanisms contribute to the prothrombotic status which characterizes DM patients underscoring the importance of antiplatelet therapies used for secondary prevention in these patients. For many years, dual antiplatelet therapy (DAPT) with aspirin and the P2Y 12 inhibitor clopidogrel has represented the mainstay of treatment following an acute coronary syndrome (ACS) or in patients undergoing percutaneous coronary interventions (PCI). Although DAPT reduces the incidence of atherothrombotic recurrences, these rates remain high in DM patients underscoring the need for more efficacious therapies. Oral platelet P2Y 12 receptor inhibitors with enhanced potency, such as prasugrel and ticagrelor, as well as antiplatelet therapies such as vorapaxar inhibiting the thrombin-mediated platelet signaling pathway, constitute treatment opportunities for patients with DM and have shown to be associated with a greater reduction in ischemic recurrences, albeit at the cost of more bleeding. This article reviews currently available antiplatelet agents and delivers an update on the advances and drawbacks of these agents used for secondary prevention in DM patients experiencing an ACS or undergoing PCI.

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“…In contrast, the CYP2C19*17 increased activity allele results in enhanced platelet inhibition and has been associated with an increased bleeding risk in some studies . However, CYP2C19 only accounts for ∼12% of the variability in clopidogrel response, which has prompted studies directed at identifying other clinical and/or genetic variables involved in on‐treatment platelet reactivity . To identify novel variants that influence on‐treatment platelet reactivity, we implemented an extreme phenotype exome sequencing pilot study.…”

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confidence: 99%

Exome sequencing of extreme clopidogrel response phenotypes identifies B4GALT2 as a determinant of on‐treatment platelet reactivity

Scott

Collet

Baber

et al. 2016

Clin Pharma and Therapeutics

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Interindividual variability in platelet aggregation is common among patients treated with clopidogrel, and both high and low (LTPR) on-treatment platelet reactivity increase risks for adverse clinical outcomes. CYP2C19 influences clopidogrel response but only accounts for ~12% of the variability in platelet reactivity. To identify novel variants implicated in on-treatment platelet reactivity, coronary artery disease (CAD) patients with extreme pharmacodynamic responses to clopidogrel and wild-type CYP2C19 were subjected to exome sequencing. Candidate variants that clustered in the LTPR subgroup subsequently were genotyped across the discovery cohort (n=636). Importantly, carriers of B4GALT2 c.909C>T had lower on-treatment P2Y12 reaction units (PRU; p=0.0077) and residual platelet aggregation (p=0.0008) compared to non-carriers, which remained significant after adjusting for CYP2C19 and other clinical variables in both the discovery (p=0.0298) and replication (n=160; PRU: p=0.0001) cohorts. B4GALT2 is a platelet-expressed galactosyltransferase, indicating that B4GALT2 c.909C>T may influence clopidogrel sensitivity through atypical cell-surface glycoprotein processing and platelet adhesion.

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Pharmacogenetics in Cardiovascular Antithrombotic Therapy

Cited by 16 publications

References 89 publications

Genetic Cardiovascular Risk Prediction

Genetic Cardiovascular Risk Prediction

Diabetes and antiplatelet therapy: from bench to bedside

Exome sequencing of extreme clopidogrel response phenotypes identifies B4GALT2 as a determinant of on‐treatment platelet reactivity

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