Current Molecular Medicine (v.14, #7)

The sequencing of the first human genome in 2001 highlighted remarkable complexity and heterogeneity [1] andbrought great anticipation in advancing our understanding of disease. The therapeutic promise implicit in researchventures like the Human Genome Project (HGP) and other advancements in genetic-genomic DNA technology lieswithin the concept of personalized medicine. A key element of personalized medicine is to develop medicaltreatment 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 drugmetabolism, therapeutic response, and adverse reactions (i.e., pharmacokinetics and pharmacodynamics). Thegenetic 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 haspromoted the advancement of molecular medicine, and started to bridge the gap between science and medicinethrough a shared progression across a variety of disciplines. This collection of reviews introduces the field of datascience, along with the latest experimental approaches and statistical methods being used to analyze the vastamounts of large-scale, genome-based data from pharmacogenetic-pharmacogenomic studies (Penrod andMoore). Furthermore, genome-wide association studies (GWAS) are outlined as a powerful and effective tool toidentify susceptibility loci and targeted pharmacotherapies for complex diseases, such as age-related maculardegeneration (AMD) (Rosen, Kaushal, and SanGiovanni). Similarly, the utility of lymphoblastoid cell lines (LCLs) isreviewed as an efficient model system for performing human pharmacogenomic studies in vitro (Jack, Rotroff, andMotsinger-Reif). In terms of clinical studies, the latest pharmacogenetic-pharmacogenomic applications relating toneurological disorders, including Parkinson's and Alzheimer's disease, as well as common mental illnesses, suchas schizophrenia (SCZ), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) areoutlined (Gilman and Mao). The growing field of anti-obesity medications, together with the genes and gene variantsthought to impact their effectiveness is also presented (Guzman and Martin). Among a wide array of cardiovascularrelatedtopics, the timely issue of "aspirin resistance", along with the cardiovascular risks associated with nonsteroidalanti-inflammatory drugs is explored, as are the underlying genetic factors affecting antithrombotic agents incoronary artery disease and ischemic stroke (Stitham and Hwa). Furthermore, there is a focused review examiningthe latest U.S. and European clinical trials regarding pharmacogenetic-guided warfarin dosing (Baranova andMaitland-van der Zee), as well as a detailed look into genetic variability and its relation to antihypertensive and lipidloweringmedications (Vanichakarn and Stitham).Some of the major obstacles facing pharmacogenetic and pharmacogenomic research, as well as itsimplementation to mainstream clinical practice are also discussed. In particular, a common hindrance revealed inthe series is the lack of consistency and reproducibility across studies. While differences in study design, smallsample size, and heterogeneity among patient populations have been noted, the complexity within the genetic basisof disease and heritability is staggering. Even with monogenic disorders, issues such as pleiotropy, variable orincomplete penetrance, as well as inconsistent expressivity, can make genotype-phenotype associations quitedifficult [2]. Moreover, these same issues are compounded by the multifaceted nature of polygenic diseases, andcoupled with a myriad of potential environmental influences adding to the complexity [3]. As outlined in this reviewseries, tremendous progress has been made to address these limitations however further cross-disciplinarycollaborations are needed. The exponential expansion of information (tens of thousands of publications beingadded 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 topay 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 diseasesusceptibility, progression, and/or treatment outcomes. Moreover, these advancements have provided tremendousinsights 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 tobe done. We are “so close and yet so far”.

Data Science Approaches to Pharmacogenetics by N.M. Penrod, J.H. Moore (805-813).
Pharmacogenetic studies rely on applied statistics to evaluate genetic data describing naturalvariation in response to pharmacotherapeutics such as drugs and vaccines. In the beginning, these studieswere based on candidate gene approaches that specifically focused on efficacy or adverse events correlatedwith variants of single genes. This hypothesis driven method required the researcher to have a prioriknowledge of which genes or gene sets to investigate. According to rational design, the focus of these studieshas 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 canbe measured in large scale, population based, genome-wide association studies (GWAS). This enablesidentification of novel genetic biomarkers, therapeutic targets, and analysis of gene-gene interactions, whichmay reveal molecular mechanisms of drug activities. Ultimately, the challenge is to utilize gene-drugassociations to create dosing algorithms based individual genotypes, which will guide physicians and ensurethey 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 pharmacologicoutcomes.

Genome-wide association (GWA) studies apply broad DNA scans on hundreds-of-thousands ofcommon 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 pharmacogenomicsresearch. 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-targettoDNA sequence variant-to disease approach provided guidance on rational design of preclinical studies andidentified associations between: 1) valproic acid and advanced AMD-associated genes with the capacity toalter 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 altersystems involved in cAMP-PKA signaling. In both applications of our method, the breadth of GWA findingsallowed efficient expansion of results to identify enriched pathways and additional ligands capable of targetingpathway constituents. A disease associated SNP-to gene-to target-to ligand approach provided guidance toinform preventive and therapeutic preclinical studies investigating roles of targets in: 1) PPAR-RXRtranscription complex constituents for neovascular AMD; and 2) the stress activated MAPK signaling cascadeconstituents for advanced AMD. Our conclusion is that publically available data from GWA studies can be usedsuccessfully with open-access genomics, proteomics, structural chemistry, and pharmacogenomics databasesin an efficient, rational approach to streamline the processes of planning and implementation for confirmatoryand exploratory pre-clinical studies of preventive or therapeutic pharmacologic treatments for complexdiseases.

A new standard for medicine is emerging that aims to improve individual drug responses throughstudying associations with genetic variations. This field, pharmacogenomics, is undergoing a rapid expansiondue 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 amodel system for human pharmacogenomic studies.;There are a wide range of publicly available resources with genome-wide data available for LCLs from bothrelated 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 vitroscreening 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. Severalmethods are discussed herein, and include multivariate concentration response modeling, issues with multipletesting, and successful examples of the 'triangle model' to identify candidate variants. Once candidate genevariants have been determined, their biological roles can be elucidated using pathway analyses andfunctionally confirmed using siRNA knockdown experiments.;The wealth of genomics data being produced using related and unrelated populations is creating many excitingopportunities leading to new insights into the genetic contribution and heritability of drug response.

The COAG and EU-PACT Trials: What is the Clinical Benefit of Pharmacogenetic-Guided Coumarin Dosing During Therapy Initiation? by E.V. Baranova, F.W. Asselbergs, A. de Boer, A.H. Maitland-van der Zee (841-848).
Coumarin derivates are oral anticoagulants commonly prescribed for treatment and prevention ofthromboembolism. Due to a small therapeutic index and large inter- and intrapatient differences in doserequirements, treatment with coumarins is challenging, particularly in its starting phase. Extensive evidencesuggests that common genetic variants in CYP2C9 and VKORC1 genes together with a number of clinicalfactors are important determinants of the coumarin dose variability. Pharmacogenetic algorithms comprisingboth 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 dosingof warfarin, acenocoumarol and phenprocoumon were published. In these trials different coumarin dosingstrategies were compared to investigate whether or not pharmacogenetic testing could be beneficial forcoumarin management. The purpose of this review was to present and discuss the design and results of thesestudies within the context of previously published randomized controlled trials and to address the issuessurrounding the incorporation of coumarin pharmacogenetic testing into clinical practice.

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

The Applications of Pharmacogenomics to Neurological Disorders by C. Gilman, C. McSweeney, Y. Mao (880-890).
The most common neurological disorders, including neurodegenerative diseases and psychiatricdisorders, 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 recentadvances in two disorders, Parkinson's disease (PD) and Alzheimer's diseases (AD), that possess both similarand 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 eachdisorder which are involved in neurodegeneration (i.e., protease pathways, calcium homeostasis, reactiveoxygen species regulation, DNA repair mechanisms, neurogenesis regulation, mitochondrial function, etc.). Inthe second part of this review, we will discuss the applications of the genome-wide technology onpharmacogenomics of mental illnesses including schizophrenia (SCZ), autism spectrum disorders (ASD),attention deficit hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD).

Pharmacogenetics of Obesity Drug Therapy by A.K. Guzman, M. Ding, Y. Xie, K.A. Martin (891-908).
As the prevalence and severity of obesity and its complications have risen significantly in worldwidepopulations, behavioral interventions alone have been inconsistent in promoting sufficient, sustained weightloss. Consequently, there has been intense interest in the development of anti-obesity medications astreatment strategies. When coupled with structured lifestyle modifications, pharmacotherapy can enhanceweight loss. While less efficacious than bariatric surgery, drug therapy may be an alternative to surgery forsome obese patients, and is an emerging strategy for weight maintenance. The goal of pharmacogenetics is tohelp identify patients who will benefit most from drug therapies while minimizing the risk of adverse effects. Inthis 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, hasbeen 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 highdegree of inter-individual variation in response to these treatments, whereby patients experience occlusivethromboembolic events, in spite of maintaining an appropriate treatment regimen. This has lead to the notion ofantithrombotic “resistance” or “poor responders”, which has been a growing concern amongst clinicians andother healthcare providers. Compounding this matter even further, reports of increased cardiovascular riskassociated 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 onunderstanding factors influencing antithrombotic and NSAID therapies. A major aim in cardiovascularpharmacogenetics is the discovery of genetic biomarkers that will allow for prospective screening andindividualized prediction of drug efficacy and adverse reactions for these medications (both alone and together)within the context of cardiovascular disease.