Pharmacogenomics: the genetics of variable drug responses

Through a glass darkly: economics and personalised medicine

Personalised medicine and pharmacogenetic-test-guided treatment strategies will be of increasing importance in the future, both in terms of healthcare provision and evaluation. It is well recognised that significant variability exists in the response of patients to drugs resulting from genetic or biological variations; however, we are only now gradually becoming aware of the complexities involved. Enormous variability occurs in the risk-benefit ratio that will be experienced by each individual patient as a consequence of their overall genetic make-up. Although not a panacea, enhanced scientific knowledge of the genetic basis for such variability offers the potential for a more 'tailored' approach to prescribing in the future, making it more closely attuned to the needs of the individual patient. Such 'personalised' medicine has the potential to revolutionise care provision in a manner that provides a range of challenges to current structures and processes of 'conventional' healthcare delivery. The aim of this paper is to outline such challenges and analyse potential ways in which they may be addressed in the future. It provides non-expert readers with a non-technical case study of the complexities inherent in the evaluation of a pharmacogenetic-test-guided treatment strategy from a health economic perspective. Wherever possible, technical issues have been minimised; however, references are provided for readers who wish to enhance their knowledge of the pharmacological basis of the case study of cytochrome P450 test-guided treatment. The case study aims simply to illustrate the approach and difficulties encountered in the health economic evaluation of complex pharmacogenetic technologies. Such technologies present a range of new and complex issues which have crucial implications for health economists attempting to obtain an accurate assessment of the 'value' of the technology in clinical practice in an array of patient subgroups. Personalised medicine is the future and this paper highlights how pharmaceutical manufacturers, clinicians, regulators and other stakeholders must all play their part in the inevitable and accelerating move into this complex and uncertain future.

Potential applications of pharmacogenomics to heart failure therapies

Pharmacogenomics explores one drug's varying effects on different patient genotypes. A better understanding of genomic variation's contribution to drug response can impact 4 arenas in heart failure (HF): (1) identification of patients most likely to receive benefit from therapy, (2) risk stratify patients for risk of adverse events, (3) optimize dosing of drugs, and (4) steer future clinical trial design and drug development. In this review, the authors explore the potential applications of pharmacogenomics in patients with HF in the context of these categories.

Single nucleotide polymorphism and its dynamics for pharmacogenomics

Pharmacogenomics is the study of how the genetic makeup determines the response to a therapeutic intervention. It has the capability to revolutionize the practice of medicine by personalized approach for treatment through the use of novel diagnostic tools. Pharmacogenomic based approaches reduce the trial-and-error approach and restrict the exposure of patients to those drugs which are not effective or are toxic for them. Single Nucleotide Polymorphisms (SNPs) hold the key in defining the risk of an individual's susceptibility to various illnesses and response to drugs. There is an ongoing process of identifying the common, biologically relevant SNPs, in particular those that are associated with the risk of disease and adverse drug reaction. The identification and characterization of these SNPs are necessary before their use as genetic tools. Most of the ongoing SNP related studies are biased deliberately towards coding regions and the data generated from them are therefore unlikely to reflect genome wide distribution of SNPs. To avoid this biasing towards the coding regions SNP, SNP consortium protocol was designed. Though, projects like the HapMap increase credibility and use of SNPs, still there are some concern like the required sample (patient) sizes, the number of SNPs required for mapping, number of association studies, the cost of SNP genotyping, and the interpretation and explanation of results are some of the challenges that surround this field.

An ontology-based, mobile-optimized system for pharmacogenomic decision support at the point-of-care

Background

The development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects.

Results

We developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities.

Conclusions

The MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine.

Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval

Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the 'missing heritability' of complex traits. Here, we describe four independent analyses in 33 781 participants of European ancestry from 10 cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0%), tri/tetracyclic antidepressants (2.6%), sulfonylurea hypoglycemic agents (2.9%) and QT-prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4%). Drug-gene interactions were estimated using covariable-adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-single-nucleotide polymorphism (SNP) interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0 × 10(-8)). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

Pharmacogenomics: historical perspective and current status

Pharmacogenomics and its predecessor pharmacogenetics study the contribution of genetic factors to the interindividual variability in drug efficacy and safety. One of the major goals of pharmacogenomics is to tailor drugs to individuals based on their genetic makeup and molecular profile. From early findings in the 1950s uncovering inherited deficiencies in drug metabolism that explained drug-related adverse events, to nowadays genome-wide approaches assessing genetic variation in multiple genes, pharmacogenomics has come a long way. The evolution of pharmacogenomics has paralleled the evolution of genotyping technologies, the completion of the human genome sequencing and the HapMap project. Despite these advances, the implementation of pharmacogenomics in clinical practice has yet been limited. Here we present an overview of the history and current applications of pharmacogenomics in patient selection, dosing, and drug development with illustrative examples of these categories. Some of the challenges in the field and future perspectives are also presented.

Insights from genome-wide association studies of drug response

Early genome-wide association studies (GWAS) using relatively small samples have identified both rare and common genetic variants with large impact on severe adverse drug reactions, dosing, and efficacy. Here we outline the challenges and recent successes of the GWAS approach in disease genetics and the ways in which these can be applied to pharmacogenomics for biological discovery, determination of heritability, and personalized treatment. We highlight that the genetic architecture of drug efficacy reflects a complex trait yet that of adverse drug reactions more closely mirrors the architecture of Mendelian diseases and how this difference affects future study design. Given that multiple layers of biological data are increasingly available on large samples from biorepositories linked to electronic medical records, GWAS will remain a key component of the systems biology approach to uncovering small to moderate genetic determinants of drug response; these discoveries should move us closer to a personalized approach to health care.

The pharmacogenomics of sex hormone metabolism: breast cancer risk in menopausal hormone therapy

With women in western countries spending nearly one-third of their lifetime beyond menopause and a substantial number of these women facing severe menopausal symptoms, the goal of sex hormone pharmacogenomics is to promote the safe use of hormone replacement therapy (HRT). This could be achieved by providing molecular predictors for the upfront stratification of women in need of relief from menopausal symptoms into those with a likely benefit from HRT and those with a contraindication due to an HRT-associated breast cancer risk or other adverse effects. An increasing knowledge base of sex hormone metabolism and its variability, HRT outcomes and breast cancer susceptibility, as well as emerging examples of pharmacogenomic predictors, underscore the potential relevance of genetic variations for HRT outcome. The genes responsible for the metabolism, signaling and action of sex hormones are at the heart of this research; however, pharmacogenomic investigation of their therapeutic effects due to the enormous complexity of the biological pathways involved is still in its infancy. This article discusses the current knowledge, challenges and potential future directions towards the goal of genotype-guided safer HRT use.

Strategies for personalized medicine-based research and implementation in the clinical workflow

The decoding of the human genome, paralleled by the development of high-throughput technologies to obtain large-scale molecular data (e.g., information on genetic variations, transcription levels, and metabolite concentrations) is providing new insights into the molecular basis of common diseases and the causes of variability in drug response. This article presents strategies to incorporate this new knowledge into research and clinical workflow.

A large candidate gene survey identifies the KCNE1 D85N polymorphism as a possible modulator of drug-induced torsades de pointes

BACKGROUND

Drug-induced long-QT syndrome (diLQTS) is an adverse drug effect that has an important impact on drug use, development, and regulation. We tested the hypothesis that common variants in key genes controlling cardiac electric properties modify the risk of diLQTS.

METHODS AND RESULTS

In a case-control setting, we included 176 patients of European descent from North America and Europe with diLQTS, defined as documented torsades de pointes during treatment with a QT-prolonging drug. Control samples were obtained from 207 patients of European ancestry who displayed <50 ms QT lengthening during initiation of therapy with a QT-prolonging drug and 837 control subjects from the population-based KORA study. Subjects were successfully genotyped at 1424 single-nucleotide polymorphisms (SNPs) in 18 candidate genes including 1386 SNPs tagging common haplotype blocks and 38 nonsynonymous ion channel gene SNPs. For validation, we used a set of cases (n=57) and population-based control subjects of European descent. The SNP KCNE1 D85N (rs1805128), known to modulate an important potassium current in the heart, predicted diLQTS with an odds ratio of 9.0 (95% confidence interval, 3.5-22.9). The variant allele was present in 8.6% of cases, 2.9% of drug-exposed control subjects, and 1.8% of population control subjects. In the validation cohort, the variant allele was present in 3.5% of cases and in 1.4% of control subjects.

CONCLUSIONS

This high-density candidate SNP approach identified a key potassium channel susceptibility allele that may be associated with the rare adverse drug reaction torsades de pointes.

Mapping genes that predict treatment outcome in admixed populations

There is great interest in characterizing the genetic architecture underlying drug response. For many drugs, gene-based dosing models explain a considerable amount of the overall variation in treatment outcome. As such, prescription drug labels are increasingly being modified to contain pharmacogenetic information. Genetic data must, however, be interpreted within the context of relevant clinical covariates. Even the most predictive models improve with the addition of data related to biogeographical ancestry. The current review explores analytical strategies that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based cohorts. The success of this approach will depend upon several key factors: (1) the availability of outcome data from groups of admixed individuals (that is, populations recombined over multiple generations), (2) a measurable difference in treatment outcome (that is, efficacy and toxicity end points), and (3) a measurable difference in allele frequency between the ancestral populations.

Pharmacogenomics: will the promise be fulfilled?

Tools such as genome resequencing and genome-wide association studies have recently been used to uncover a number of variants that affect drug toxicity and efficacy, as well as potential drug targets. But how much closer are we to incorporating pharmacogenomics into routine clinical practice? Five experts discuss how far we have come, and highlight the technological, informatics, educational and practical obstacles that stand in the way of realizing genome-driven medicine.