Current Molecular Medicine - Volume 14, Issue 7, 2014

The sequencing of the first human genome in 2001 highlighted remarkable complexity and heterogeneity [1] and brought great anticipation in advancing our understanding of disease. The therapeutic promise implicit in research ventures like the Human Genome Project (HGP) and other advancements in genetic-genomic DNA technology lies within the concept of personalized medicine. A key element of personalized medicine is to develop medical treatment that is tailored to the specific disease process of each patient. Pharmacogenetics and pharmacogenomics (often used together or interchangeably) refer to the study of genetic differences and their effect on drug metabolism, therapeutic response, and adverse reactions (i.e., pharmacokinetics and pharmacodynamics). The genetic information can be used to guide clinical decision-making and optimize patient care. Highlighted in this review series are examples by which the use of pharmacogenetics and pharmacogenomics has promoted the advancement of molecular medicine, and started to bridge the gap between science and medicine through a shared progression across a variety of disciplines. This collection of reviews introduces the field of data science, along with the latest experimental approaches and statistical methods being used to analyze the vast amounts of large-scale, genome-based data from pharmacogenetic-pharmacogenomic studies (Penrod and Moore). Furthermore, genome-wide association studies (GWAS) are outlined as a powerful and effective tool to identify susceptibility loci and targeted pharmacotherapies for complex diseases, such as age-related macular degeneration (AMD) (Rosen, Kaushal, and SanGiovanni). Similarly, the utility of lymphoblastoid cell lines (LCLs) is reviewed as an efficient model system for performing human pharmacogenomic studies in vitro (Jack, Rotroff, and Motsinger-Reif). In terms of clinical studies, the latest pharmacogenetic-pharmacogenomic applications relating to neurological disorders, including Parkinson’s and Alzheimer’s disease, as well as common mental illnesses, such as schizophrenia (SCZ), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) are outlined (Gilman and Mao). The growing field of anti-obesity medications, together with the genes and gene variants thought to impact their effectiveness is also presented (Guzman and Martin). Among a wide array of cardiovascularrelated topics, the timely issue of "aspirin resistance", along with the cardiovascular risks associated with nonsteroidal anti-inflammatory drugs is explored, as are the underlying genetic factors affecting antithrombotic agents in coronary artery disease and ischemic stroke (Stitham and Hwa). Furthermore, there is a focused review examining the latest U.S. and European clinical trials regarding pharmacogenetic-guided warfarin dosing (Baranova and Maitland-van der Zee), as well as a detailed look into genetic variability and its relation to antihypertensive and lipidlowering medications (Vanichakarn and Stitham). Some of the major obstacles facing pharmacogenetic and pharmacogenomic research, as well as its implementation to mainstream clinical practice are also discussed. In particular, a common hindrance revealed in the series is the lack of consistency and reproducibility across studies. While differences in study design, small sample size, and heterogeneity among patient populations have been noted, the complexity within the genetic basis of disease and heritability is staggering. Even with monogenic disorders, issues such as pleiotropy, variable or incomplete penetrance, as well as inconsistent expressivity, can make genotype-phenotype associations quite difficult [2]. Moreover, these same issues are compounded by the multifaceted nature of polygenic diseases, and coupled with a myriad of potential environmental influences adding to the complexity [3]. As outlined in this review series, tremendous progress has been made to address these limitations however further cross-disciplinary collaborations are needed. The exponential expansion of information (tens of thousands of publications being added annually) makes incorporation of genetic markers into everyday clinical practice both needed and inevitable. Billions of dollars are being invested by both the government and private industry, and the rewards are expected to pay off in the near future [4]. More than a decade has passed since the mapping of the first human genome. Pharmacogenetic and genomic research has revealed thousands of genetic variants that contribute to disease susceptibility, progression, and/or treatment outcomes. Moreover, these advancements have provided tremendous insights into the molecular basis of many diseases, potentially leading to the development of genetic-based therapies and diagnostic tests. But as far as we have come, towards personalized medicine there remains much to be done. We are “so close and yet so far”.

Pharmacogenetic studies rely on applied statistics to evaluate genetic data describing natural variation in response to pharmacotherapeutics such as drugs and vaccines. In the beginning, these studies were based on candidate gene approaches that specifically focused on efficacy or adverse events correlated with variants of single genes. This hypothesis driven method required the researcher to have a priori knowledge of which genes or gene sets to investigate. According to rational design, the focus of these studies has been on drug metabolizing enzymes, drug transporters, and drug targets. As technology has progressed, these studies have transitioned to hypothesis-free explorations where markers across the entire genome can be measured in large scale, population based, genome-wide association studies (GWAS). This enables identification of novel genetic biomarkers, therapeutic targets, and analysis of gene-gene interactions, which may reveal molecular mechanisms of drug activities. Ultimately, the challenge is to utilize gene-drug associations to create dosing algorithms based individual genotypes, which will guide physicians and ensure they prescribe the correct dose of the correct drug the first time eliminating trial-and-error and adverse events. We review here basic concepts and applications of data science to the genetic analysis of pharmacologic outcomes.

Genome-wide association (GWA) studies apply broad DNA scans on hundreds-of-thousands of common sequence variants in thousands of people for the purpose of mapping trait- or disease-related loci. We provide examples of ligand- and target-based studies from the field of age-related macular degeneration (AMD) to demonstrate the value of the GWA approach in confirmatory and exploratory pharmacogenomics research. Complementing this genomic analysis, we used a simple biochemical retinal pigment epithelium (RPE) oxidative, apoptotic high throughput screening (HTS) assay to identify compounds. This ligand-to-targetto DNA sequence variant-to disease approach provided guidance on rational design of preclinical studies and identified associations between: 1) valproic acid and advanced AMD-associated genes with the capacity to alter GABA-succinate signaling (ALDH5A1, CACNA1C, SUCLA2, and GABBR2) and chromatin remodeling (HDAC9); and 2) Ropinirole and a geographic atrophy-associated gene (DRD3) with the capacity to alter systems involved in cAMP-PKA signaling. In both applications of our method, the breadth of GWA findings allowed efficient expansion of results to identify enriched pathways and additional ligands capable of targeting pathway constituents. A disease associated SNP-to gene-to target-to ligand approach provided guidance to inform preventive and therapeutic preclinical studies investigating roles of targets in: 1) PPAR-RXR transcription complex constituents for neovascular AMD; and 2) the stress activated MAPK signaling cascade constituents for advanced AMD. Our conclusion is that publically available data from GWA studies can be used successfully with open-access genomics, proteomics, structural chemistry, and pharmacogenomics databases in an efficient, rational approach to streamline the processes of planning and implementation for confirmatory and exploratory pre-clinical studies of preventive or therapeutic pharmacologic treatments for complex diseases.

A new standard for medicine is emerging that aims to improve individual drug responses through studying associations with genetic variations. This field, pharmacogenomics, is undergoing a rapid expansion due to a variety of technological advancements that are enabling higher throughput with reductions in cost. Here we review the advantages, limitations, and opportunities for using lymphoblastoid cell lines (LCL) as a model system for human pharmacogenomic studies. There are a wide range of publicly available resources with genome-wide data available for LCLs from both related and unrelated populations, removing the cost of genotyping the data for drug response studies. Furthermore, in contrast to human clinical trials or in vivo model systems, with high-throughput in vitro screening technologies, pharmacogenomics studies can easily be scaled to accommodate large sample sizes. An important component to leveraging genome-wide data in LCL models is association mapping. Several methods are discussed herein, and include multivariate concentration response modeling, issues with multiple testing, and successful examples of the ‘triangle model’ to identify candidate variants. Once candidate gene variants have been determined, their biological roles can be elucidated using pathway analyses and functionally confirmed using siRNA knockdown experiments. The wealth of genomics data being produced using related and unrelated populations is creating many exciting opportunities leading to new insights into the genetic contribution and heritability of drug response.

Coumarin derivates are oral anticoagulants commonly prescribed for treatment and prevention of thromboembolism. Due to a small therapeutic index and large inter- and intrapatient differences in dose requirements, treatment with coumarins is challenging, particularly in its starting phase. Extensive evidence suggests that common genetic variants in CYP2C9 and VKORC1 genes together with a number of clinical factors are important determinants of the coumarin dose variability. Pharmacogenetic algorithms comprising both genetic and non-genetic factors were developed to improve the safety of coumarin therapy initiation. Recently, three randomized controlled trials (the COAG and the EU-PACT trials) on pharmacogenetic dosing of warfarin, acenocoumarol and phenprocoumon were published. In these trials different coumarin dosing strategies were compared to investigate whether or not pharmacogenetic testing could be beneficial for coumarin management. The purpose of this review was to present and discuss the design and results of these studies within the context of previously published randomized controlled trials and to address the issues surrounding the incorporation of coumarin pharmacogenetic testing into clinical practice.

Recent changes to the clinical management guidelines for hypertension and hyperlipidemia have placed emphasis on prevention through the pharmacological control and reduction of cardiovascular risk factors. In conjunction with proper diet and lifestyle changes, such risk factor control necessitates the use of safe and effective pharmacotherapy. However, many patients fail to reach or maintain therapeutic goals due to inadequacy and/or variability in response to antihypertensive and lipid-lowering medications. Thus, given the contribution of both hypertension and hyperlipidemia in the development and progression of cardiovascular disease, a personalized approach to pharmacotherapy, as well as disease prevention, seems particularly prudent. With the advancement of cardiovascular pharmacogenetics, the aim is to identify genetic biomarkers of drug-response and disease-susceptibility in order to make informed and individualized decisions, improving patient care through proper drug selection and dosing.

The most common neurological disorders, including neurodegenerative diseases and psychiatric disorders, have received recent attention with regards to pharmacogenomics and personalized medicine. Here, we will focus on a neglected neurodegenerative disorder, cerebral ischemic stroke (CIS), and highlight recent advances in two disorders, Parkinson’s disease (PD) and Alzheimer’s diseases (AD), that possess both similar and distinct mechanisms in regards to potential therapeutic targets. In the first part of this review, we will focus primarily on mechanisms that are somewhat specific to each disorder which are involved in neurodegeneration (i.e., protease pathways, calcium homeostasis, reactive oxygen species regulation, DNA repair mechanisms, neurogenesis regulation, mitochondrial function, etc.). In the second part of this review, we will discuss the applications of the genome-wide technology on pharmacogenomics of mental illnesses including schizophrenia (SCZ), autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD).

As the prevalence and severity of obesity and its complications have risen significantly in worldwide populations, behavioral interventions alone have been inconsistent in promoting sufficient, sustained weight loss. Consequently, there has been intense interest in the development of anti-obesity medications as treatment strategies. When coupled with structured lifestyle modifications, pharmacotherapy can enhance weight loss. While less efficacious than bariatric surgery, drug therapy may be an alternative to surgery for some obese patients, and is an emerging strategy for weight maintenance. The goal of pharmacogenetics is to help identify patients who will benefit most from drug therapies while minimizing the risk of adverse effects. In this review, we summarize the pharmacogenetic literature on obesity drugs of the past (sibutramine, rimonabant), present (orlistat, lorcaserin, phentermine, topiramate), and future (buprioprion/naltrexone).

The use of antithrombotic agents, particularly antiplatelet drugs like aspirin and clopidogrel, has been instrumental in decreasing the risk for adverse cardiovascular events across a wide range of patients. However, despite the established benefits, the use of these medications remains suboptimal. There is a high degree of inter-individual variation in response to these treatments, whereby patients experience occlusive thromboembolic events, in spite of maintaining an appropriate treatment regimen. This has lead to the notion of antithrombotic “resistance” or “poor responders”, which has been a growing concern amongst clinicians and other healthcare providers. Compounding this matter even further, reports of increased cardiovascular risk associated with the use of non-steroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen and naproxen, have revealed additional and unforeseen contributors to myocardial infarction and stroke. With all medications, striking a balance between the potential risks and benefits seems more art than science at times. However, given their widespread use and critical cardiovascular implications, further emphasis has been placed on understanding factors influencing antithrombotic and NSAID therapies. A major aim in cardiovascular pharmacogenetics is the discovery of genetic biomarkers that will allow for prospective screening and individualized prediction of drug efficacy and adverse reactions for these medications (both alone and together) within the context of cardiovascular disease.