Pharmacogenetics in cardiovascular disease: the challenge of moving from promise to realization: concepts discussed at the Canadian Network and Centre for Trials Internationally Network Conference (CANNeCTIN), June 2009

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

Prognostic impact of carboxylesterase 1 gene variants in patients with congestive heart failure treated with angiotensin-converting enzyme inhibitors

OBJECTIVE

Most angiotensin-converting enzyme inhibitors (ACEIs) are prodrugs activated by carboxylesterase 1 (CES1). We investigated the prognostic importance of CES1 gene (CES1) copy number variation and the rs3815583 single-nucleotide polymorphism in CES1 among ACEI-treated patients with congestive heart failure (CHF).

METHODS

Danish patients with chronic CHF enrolled in the previously reported Echocardiography and Heart Outcome Study were categorized according to their CES1 variants and followed up for up to 10 years. Risk for cardiovascular death and all-cause death was modeled by Cox proportional hazard analyses.

RESULTS

A total of 491 ACEI-treated patients were included in the analyses. After a mean follow-up of 5.5 years, we found no difference in the risk for cardiovascular death and all-cause death between patients having three [hazard ratios (HRs) 1.06 (95% confidence interval (CI) 0.77-1.45) and 1.16 (95% CI 0.88-1.52)] or four [HRs 0.88 (95% CI 0.39-2.01) and 1.37 (95% CI 0.74-2.54)] CES1 copies and those with two copies, respectively. Similarly, no difference in the risk for cardiovascular and all-cause death was found for patients heterozygous [HRs 0.91 (95% CI 0.70-1.19) and 0.88 (95% CI 0.69-1.12)] or homozygous [HRs 0.58 (95% CI 0.30-1.15) and 0.82 (95% CI 0.48-1.39)] for the rs3815583 minor allele versus patients homozygous for the major allele. The active promoter of CES1A2 and the rs71647871 single-nucleotide polymorphism minor allele were detected at very low frequencies.

CONCLUSION

This study did not support the use of CES1 copy number variation or rs3815583 as a predictor of fatal outcomes in ACEI-treated patients with CHF.

Pharmacogenetic Risk Stratification in Angiotensin-Converting Enzyme Inhibitor-Treated Patients with Congestive Heart Failure: A Retrospective Cohort Study

Background

Evidence for pharmacogenetic risk stratification of angiotensin-converting enzyme inhibitor (ACEI) treatment is limited. Therefore, in a cohort of ACEI-treated patients with congestive heart failure (CHF), we investigated the predictive value of two pharmacogenetic scores that previously were found to predict ACEI efficacy in patients with ischemic heart disease and hypertension, respectively. Score A combined single nucleotide polymorphisms (SNPs) of the angiotensin II receptor type 1 gene (rs275651 and rs5182) and the bradykinin receptor B1 gene (rs12050217). Score B combined SNPs of the angiotensin-converting enzyme gene (rs4343) and ABO blood group genes (rs495828 and rs8176746).

Methods

Danish patients with CHF enrolled in the previously reported Echocardiography and Heart Outcome Study were included. Subjects were genotyped and categorized according to pharmacogenetic scores A and B of ≤1, 2 and ≥3 each, and followed for up to 10 years. Difference in cumulative incidences of cardiovascular death and all-cause death were assessed by the cumulative incidence estimator. Survival was modeled by Cox proportional hazard analyses.

Results

We included 667 patients, of whom 80% were treated with ACEIs. Differences in cumulative incidences of cardiovascular death (P = 0.346 and P = 0.486) and all-cause death (P = 0.515 and P = 0.486) were not significant for score A and B, respectively. There was no difference in risk of cardiovascular death or all-cause death between subjects with score A ≤1 vs. 2 (HR 1.03 [95% CI 0.79–1.34] and HR 1.11 [95% CI 0.88–1.42]), score A ≤1 vs. ≥3 (HR 0.80 [95% CI 0.59–1.08] and HR 0.91 [95% CI 0.70–1.20]), score B ≤1 vs. 2 (HR 1.02 [95% CI 0.78–1.32] and HR 0.98 [95% CI 0.77–1.24]), and score B ≤1 vs. ≥3 (HR 1.03 [95% CI 0.75–1.41] and HR 1.05 [95% CI 0.79–1.40]), respectively.

Conclusions

We found no association between either of the analyzed pharmacogenetic scores and fatal outcomes in ACEI-treated patients with CHF.

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.

Commonly used diabetes and cardiovascular medications and cancer recurrence and cancer-specific mortality: a review of the literature

INTRODUCTION

Cancer most commonly arises in the elderly who are often burdened with comorbidities. Medications used for treating these comorbidities may alter cancer prognosis. Understanding the impact of these medications on cancer is important in order to make effective evidence-based decisions about managing comorbidities while improving cancer outcomes.

AREAS COVERED

The evidence on diabetes, statins, antihypertensive and anti-inflammatory medications and their association with cancer recurrence and cancer-specific mortality are reviewed. The strengths and limitations of the existing literature, the current state of the field and future directions are discussed.

EXPERT OPINION

Metformin and aspirin were associated with a reduced risk of cancer recurrence and cancer-specific mortality. The evidence for statins and antihypertensive medications on cancer survival was inconsistent. There were few studies to suggest that any of the medication classes of interest were associated with negative effects on cancer survival. Methodological shortcomings within observational studies, such as confounding, distinguishing between use of medications pre-cancer versus post-cancer diagnosis/treatment, misclassification of exposures/outcomes, informative censoring and competing risks, must be considered. New observational studies addressing these limitations are essential. Some clinical trials are underway to further investigate the beneficial effects of these drugs and completed trials have confirmed results demonstrated in observational studies.