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This article is from Current Pharmacogenomics and Personalized Medicine , volume 9 . Abstract Pharmacogenomics is a field with origins in the study of monogenic variations in drug metabolism in the 1950s. Perhaps because of these historical underpinnings, there has been an intensive investigation of 'hepatic pharmacogenes' such as CYP450s and liver drug metabolism using pharmacogenomics approaches over the past five decades. Surprisingly, kidney pathophysiology, attendant diseases and treatment outcomes have been vastly under-studied and under-theorized despite their central importance in maintenance of health, susceptibility to disease and rational personalized therapeutics. Indeed, chronic kidney disease (CKD) represents an increasing public health burden worldwide, both in developed and developing countries. Patients with CKD suffer from high cardiovascular morbidity and mortality, which is mainly attributable to cardiovascular events before reaching end-stage renal disease. In this paper, we focus our analyses on renal function before end-stage renal disease, as seen through the lens of pharmacogenomics and human genomic variation. We herein synthesize the recent evidence linking selected Very Important Pharmacogenes (VIP) to renal function, blood pressure and salt-sensitivity in humans, and ways in which these insights might inform rational personalized therapeutics. Notably, we highlight and present the rationale for three applications that we consider as important and actionable therapeutic and preventive focus areas in renal pharmacogenomics: 1) ACE inhibitors, as a confirmed application, 2) VDR agonists, as a promising application, and 3) moderate dietary salt intake, as a suggested novel application. Additionally, we emphasize the putative contributions of gene-environment interactions, discuss the implications of these findings to treat and prevent hypertension and CKD. Finally, we conclude with a strategic agenda and vision required to accelerate advances in this under-studied field of renal pharmacogenomics with vast significance for global public health.

Manuscript review about the current knowledge in the pharmacogenomics of myofascial pain syndrome. This will serve as an initial framework in conducting future studies.

This is a registration for a Scoping Review titled: Consumer’s Recommendations for Pharmacogenomics Educational Resources: A Scoping Review

This article is from Journal of human genetics , volume 58 . Abstract The most important modality of treatment in the two-thirds of patients with an estrogen receptor (ER)-positive early breast cancer is endocrine therapy. In postmenopausal women, options include the selective ER modulators (SERMs), tamoxifen and raloxifene, and the ‘third-generation’ aromatase inhibitors (AIs), anastrozole, exemestane and letrozole. Under the auspices of the National Institutes of Health Global Alliance for Pharmacogenomics, Japan, the Mayo Clinic Pharmacogenomics Research Network Center and the RIKEN Center for Genomic Medicine have worked collaboratively to perform genome-wide association studies (GWAS) in women treated with both SERMs and AIs. On the basis of the results of the GWAS, scientists at the Mayo Clinic have proceeded with functional genomic laboratory studies. As will be seen in this review, this has led to new knowledge relating to endocrine biology that has provided a clear focus for further research to move toward truly personalized medicine for women with breast cancer.

This article is from Journal of human genetics , volume 58 . Abstract The most important modality of treatment in the two-thirds of patients with an estrogen receptor (ER)-positive early breast cancer is endocrine therapy. In postmenopausal women, options include the selective ER modulators (SERMs), tamoxifen and raloxifene, and the ‘third-generation’ aromatase inhibitors (AIs), anastrozole, exemestane and letrozole. Under the auspices of the National Institutes of Health Global Alliance for Pharmacogenomics, Japan, the Mayo Clinic Pharmacogenomics Research Network Center and the RIKEN Center for Genomic Medicine have worked collaboratively to perform genome-wide association studies (GWAS) in women treated with both SERMs and AIs. On the basis of the results of the GWAS, scientists at the Mayo Clinic have proceeded with functional genomic laboratory studies. As will be seen in this review, this has led to new knowledge relating to endocrine biology that has provided a clear focus for further research to move toward truly personalized medicine for women with breast cancer.

This article is from Journal of human genetics , volume 58 . Abstract The most important modality of treatment in the two-thirds of patients with an estrogen receptor (ER)-positive early breast cancer is endocrine therapy. In postmenopausal women, options include the selective ER modulators (SERMs), tamoxifen and raloxifene, and the ‘third-generation’ aromatase inhibitors (AIs), anastrozole, exemestane and letrozole. Under the auspices of the National Institutes of Health Global Alliance for Pharmacogenomics, Japan, the Mayo Clinic Pharmacogenomics Research Network Center and the RIKEN Center for Genomic Medicine have worked collaboratively to perform genome-wide association studies (GWAS) in women treated with both SERMs and AIs. On the basis of the results of the GWAS, scientists at the Mayo Clinic have proceeded with functional genomic laboratory studies. As will be seen in this review, this has led to new knowledge relating to endocrine biology that has provided a clear focus for further research to move toward truly personalized medicine for women with breast cancer.

In this study, we will explore and review published literature on communicating pharmacogenomic test results to pediatric patients and/or their caregivers and their expectations, fears, and attitudes towards the test results. Understanding pediatric patients' and/or their caregivers' expectations, fears, and attitudes towards the pharmacogenomic test results will help us find the gaps in current communication approaches. By gaining the knowledge and understanding of the pediatric patients' and/or caregivers' expectations, fears, and attitudes toward the test result, the study aims to develop a communication strategy framework to optimize and standardize care in pediatric pharmacogenomics. Effective communication strategies for healthcare providers are warranted to set up realistic expectations for the evidence-based use of pharmacogenomic test results to empower patients to make positive health care decisions.

This systematic review aims to synthesize research on the genetic basis of cluster headaches, particularly through studies on genetic markers, gene expression, and pharmacogenomics. The review will explore specific alleles, gene variants, and mutations identified through targeted gene studies and genome-wide association studies (GWAS). Additionally, it will examine how gene expression profiles contribute to the understanding of the pathophysiology of cluster headaches. Pharmacogenomics research will be reviewed to assess how genetic differences affect individual responses to treatments, with a focus on personalized management strategies.

This article is from BMC Genetics , volume 15 . Abstract Background: African Americans have been treated as a representative population for African ancestry for many purposes, including pharmacogenomic studies. However, the contribution of European ancestry is expected to result in considerable differences in the genetic architecture of African American individuals compared with an African genome. In particular, the genetic admixture influences the genomic diversity of drug metabolism-related genes, and may cause high heterogeneity of drug responses in admixed populations such as African Americans. Results: The genomic ancestry information of African-American (ASW) samples was obtained from data of the 1000 Genomes Project, and local ancestral components were also extracted for 32 core genes and 252 extended genes, which are associated with drug absorption, distribution, metabolism, and excretion (ADME) genes. As expected, the global genetic diversity pattern in ASW was determined by the contributions of its putative ancestral source populations, and the whole profiles of ADME genes in ASW are much closer to those in YRI than in CEU. However, we observed much higher diversity in some functionally important ADME genes in ASW than either CEU or YRI, which could be a result of either genetic drift or natural selection, and we identified some signatures of the latter. We analyzed the clinically relevant polymorphic alleles and haplotypes, and found that 28 functional mutations (including 3 missense, 3 splice, and 22 regulator sites) exhibited significantly higher differentiation between the three populations. Conclusions: Analysis of the genetic diversity of ADME genes showed differentiation between admixed population and its ancestral source populations. In particular, the different genetic diversity between ASW and YRI indicated that the ethnic differences in pharmacogenomic studies are broadly existed despite that African ancestry is dominant in Africans Americans. This study should advance our understanding of the genetic basis of the drug response heterogeneity between populations, especially in the case of population admixture, and have significant implications for evaluating potential inter-population heterogeneity in drug treatment effects.

This article is from BMC Genetics , volume 15 . Abstract Background: African Americans have been treated as a representative population for African ancestry for many purposes, including pharmacogenomic studies. However, the contribution of European ancestry is expected to result in considerable differences in the genetic architecture of African American individuals compared with an African genome. In particular, the genetic admixture influences the genomic diversity of drug metabolism-related genes, and may cause high heterogeneity of drug responses in admixed populations such as African Americans. Results: The genomic ancestry information of African-American (ASW) samples was obtained from data of the 1000 Genomes Project, and local ancestral components were also extracted for 32 core genes and 252 extended genes, which are associated with drug absorption, distribution, metabolism, and excretion (ADME) genes. As expected, the global genetic diversity pattern in ASW was determined by the contributions of its putative ancestral source populations, and the whole profiles of ADME genes in ASW are much closer to those in YRI than in CEU. However, we observed much higher diversity in some functionally important ADME genes in ASW than either CEU or YRI, which could be a result of either genetic drift or natural selection, and we identified some signatures of the latter. We analyzed the clinically relevant polymorphic alleles and haplotypes, and found that 28 functional mutations (including 3 missense, 3 splice, and 22 regulator sites) exhibited significantly higher differentiation between the three populations. Conclusions: Analysis of the genetic diversity of ADME genes showed differentiation between admixed population and its ancestral source populations. In particular, the different genetic diversity between ASW and YRI indicated that the ethnic differences in pharmacogenomic studies are broadly existed despite that African ancestry is dominant in Africans Americans. This study should advance our understanding of the genetic basis of the drug response heterogeneity between populations, especially in the case of population admixture, and have significant implications for evaluating potential inter-population heterogeneity in drug treatment effects.

This paper examines the transformative impact of artificial intelligence (AI) on pharmacogenomics, signaling a paradigm shift in personalized medicine. With a focus on enhancing drug response prediction and treatment optimization, AI, particularly machine learning and deep learning algorithms, navigates the complexity of genomic data. By elucidating intricate relationships between genetic factors and drug responses, AI augments the identification of genetic markers and contributes to the development of comprehensive models. The review emphasizes AI's role in guiding treatment decisions, minimizing adverse reactions, and optimizing drug dosages in clinical settings. Ethical considerations, challenges, and future directions are also discussed. This work underscores the synergy of AI and pharmacogenomics, offering a more effective and patient-centric approach to drug therapy, marking a significant advancement in the field of personalized medicine.

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This scoping review explores the current state of pharmacogenomics research in childhood cancers across Africa. Childhood cancer is a major public health concern, particularly in lower and middle-income countries, where cure rates lag significantly behind those of developed nations. The review aims to map the existing literature, identify key findings, and highlight research gaps concerning the role of genetic variants, such as those affecting cytochrome P450 and anticancer drug-metabolizing genes, in influencing treatment outcomes in African pediatric cancer patients. By examining this emerging field, the review seeks to promote pharmacogenomics as a tool for improving cure rates and treatment outcomes in African children with cancer.

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This article is from BMC Genomics , volume 15 . Abstract None

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This article is from Yeast (Chichester, England) , volume 17 . Abstract Pharmacogenomics is defined as the study of the association between genetics and drug response. This is a rapidly expanding field with the hope that, within a few years, prospective genotyping will lead to patients being prescribed drugs which are both safer and more effective (‘the right drug for the right patient’, or personalized medicine). There are many existing examples in the literature of strong associations between genetic variation and drug response, and some of these even form the basis of accepted clinical tests. The molecular basis for some of these associations is described, and includes examples of variation in genes responsible for absorption and metabolism of the drug, and in target and disease genes. However, there are many issues surrounding the legal, regulatory and ethical framework to these studies that remain unanswered, and a huge amount of education both for the public and haelthcare professionals will be needed bafore the results of this new madicine can be widely accepted.

This article is from Indian Journal of Nephrology , volume 23 . Abstract In patients undergoing renal transplantation, dose individualization for tacrolimus is routinely achieved with therapeutic drug monitoring (TDM). The patient started on 5.5 mg/day of tacrolimus had a significantly elevated tacrolimus trough concentration. The tacrolimus dose was regularly reduced following TDM at many time periods in the post transplant period but the tacrolimus concentration was consistently elevated. Genomic analysis done after four years revealed mutations in the genes encoding for CYP3A5 and MDR1 (2677G > T). Pharmacogenomics alongside TDM, will soon emerge as the backbone of dose individualization. But for genomics to be beneficial, it should be advocated in the pre-transplant or early post transplant period.

With thousands of experts from Mayo Clinic campuses in Minnesota, Florida, Arizona, and the Mayo Clinic Health System, Mayo Clinic Radio reaches listeners with an easy-to-understand, friendly approach. High interest topics include the latest news as well as information about exercise, nutrition, prevention and heart health. with your Mayo Clinic Radio Health Minute, iâm tracy mccray. we hear a lot about individualized medicine these days, tailoring treatment and prevention strategies to achieve the best results in each patient. One of the best examples is an area called pharmacogenomics. Dr. Eric Green is director of the national human genome institute ânow we can open up a personâs genome, look inside and say, wow, youâre going to be a good responder or youâre going to be a bad responder to this medication. before we give you that medication, letâs learn that.â Dr. richard weinshilboum is director of the phamacogenomics program at mayo clinicâs center for individualized medicine âthis part of genomic medicine is ready for primetime. the food and drug administration has a website where they list gene drug pairs that they say physicians need to know about.â Dr. Wienshilbaum says the challenge is making that information readily available to physicians in a user friendly model. for mayo clinic radio, iâm tracy mccray.

This article is from Dialogues in Clinical Neuroscience , volume 7 . Abstract Genetic factors are believe y a major role in the variation of treatment response and the incidence of adverse effects to medication. The aim of pharmacogenetics is to elucidate this variability according to hereditary differences. Considering current hypotheses for the mechanisms of action of antidepressants, most investigations to date have concentrated on mutations in genes coding either for the pathways in the serotonergic and noradrenergic systems or for drug-metabolizing enzymes. Recent studies shifted the emphasis on the mains mechanism of drug action from changes in neurotransmitter concentration or receptor function toward long-lasting adaptive processes within the neurons. Although the results are controversial, many studies support the hypothesis that psychopharmacogenetics will help predict an individual's drug response, while minimizing the side effects. The inclusion of functional genomics, investigate the complex gene and/or protein expression in response to a given drug, may lead to the development of novel and safer drugs.

This article is from Dialogues in Clinical Neuroscience , volume 7 . Abstract Genetic factors are believe y a major role in the variation of treatment response and the incidence of adverse effects to medication. The aim of pharmacogenetics is to elucidate this variability according to hereditary differences. Considering current hypotheses for the mechanisms of action of antidepressants, most investigations to date have concentrated on mutations in genes coding either for the pathways in the serotonergic and noradrenergic systems or for drug-metabolizing enzymes. Recent studies shifted the emphasis on the mains mechanism of drug action from changes in neurotransmitter concentration or receptor function toward long-lasting adaptive processes within the neurons. Although the results are controversial, many studies support the hypothesis that psychopharmacogenetics will help predict an individual's drug response, while minimizing the side effects. The inclusion of functional genomics, investigate the complex gene and/or protein expression in response to a given drug, may lead to the development of novel and safer drugs.

The landscape of modern healthcare is rapidly evolving, with personalized medicine emerging as a transformative approach to patient care4. Pharmacogenomics, a critical component of this paradigm shift, offers the potential to tailor medical interventions based on individual genetic profiles. This approach has demonstrated significant benefits in various therapeutic areas, including cardiovascular medicine, oncology, and psychiatry, where genetic variations can substantially impact drug response and safety profiles. Pharmacists, with their expertise in medication management and patient counseling, are increasingly recognized as key stakeholders in the successful implementation of pharmacogenomics. Their specific contributions include: 1. Interpreting genetic test results and their clinical implications 2. Providing medication therapy management based on genetic information 3. Conducting patient education and counseling about genetic testing 4. Collaborating with healthcare providers to optimize drug therapy 5. Developing clinical decision support tools for genetic-guided prescribing 6. Managing pharmacogenomic testing programs Pharmacists' deep understanding of pharmacotherapy, coupled with their accessibility to patients, positions them to contribute significantly to pharmacogenomics implementation. Their involvement can potentially enhance medication safety by minimizing adverse drug reactions, optimizing drug selection and dosing, and improving patient adherence to personalized treatment plans. Evidence suggests that pharmacist-led pharmacogenomic services can reduce adverse drug events by 15-20% and improve medication adherence rates by up to 30%. Despite growing interest and emerging evidence supporting pharmacist involvement in pharmacogenomics, several knowledge gaps remain including limited synthesis of evidence regarding optimal models for pharmacist-led pharmacogenomic services, unclear understanding of implementation barriers and facilitators across different practice settings, a lack of comprehensive evaluation of the impact on patient outcomes and healthcare costs, and insufficient documentation of successful integration strategies into existing pharmacy workflows. This scoping review aims to address these gaps by systematically mapping and synthesizing the existing evidence on pharmacists' roles in implementing pharmacogenomics within personalized medicine. Specifically, we will explore the current landscape of pharmacist-led pharmacogenomic applications, identify implementation strategies and barriers, and synthesize evidence on the impact of pharmacist involvement on treatment outcomes and patient care.

Dyslipidemia is a key contributor to cardiovascular diseases, especially in obese individuals, where lipid metabolism is frequently impaired. Lipid-lowering therapies, such as statins, fibrates, and PCSK9 inhibitors, are commonly prescribed to manage dyslipidemia, but their efficacy and adverse effect profiles vary significantly across individuals. Pharmacogenomics, the study of how genetic variations influence drug response, has emerged as a powerful tool in predicting these inter-individual differences and optimizing treatment regimens. In obese individuals, genetic polymorphisms in genes involved in lipid metabolism, such as SLCO1B1, CYP3A4, APOE, and LDLR, have been linked to variable responses to lipid-lowering drugs. This review explores the impact of pharmacogenomics on lipid-lowering therapies in obese individuals with dyslipidemia, emphasizing the potential of personalized medicine in improving therapeutic outcomes. By understanding genetic influences on drug efficacy and safety, clinicians can tailor treatments to individual patients, thereby reducing adverse effects and improving lipid control. We also highlight current challenges in translating pharmacogenomic findings into clinical practice and discuss the future directions of this emerging field. 

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) is significantly transforming the landscape of pharmaceutical biotechnology and pharmacogenomics. These technologies are redefining how drugs are discovered, tested, and personalized for individual patients based on their genetic profiles. AI algorithms are now being used to identify potential drug candidates, predict molecular interactions, optimize clinical trials, and tailor therapies to enhance patient outcomes. In the realm of pharmacogenomics, ML models facilitate the interpretation of complex genomic data, enabling clinicians to anticipate drug responses and minimize adverse effects. While the benefits are substantial—ranging from faster innovation cycles to more precise medical interventions—the integration of AI also presents several challenges, including data quality issues, interpretability: of models, regulatory ambiguity, and ethical concerns around privacy and bias. This paper explores the current applications, tools, benefits, limitations, and future directions of AI and ML in pharmaceutical biotechnology and genomic medicine, emphasizing the potential of these technologies to drive a new era of personalized and predictive healthcare.

Statins are the most widely used lipid-lowering drugs across the globe for the treatment of hyperlipidemia. The aims of this scoping review are to: (i) identify the genetic variants associated with statins treatment responses among Asian populations, and (ii) explore the effects of these genetic variants on drug efficacy and susceptibility to statin-induced adverse drug reactions (ADR).

Current knowledge on the pharmacogenomics of myofascial pain syndrome

INTRODUCTION: Future spaceflight will require increased crew medical autonomy as exploration class missions expand in duration and distance from Earth, especially for Mars missions. As mission duration increases, it will be essential to have appropriate amounts of effective medication to ensure the maintenance of crew health and performance. Conversely, mass and volume constraints will become more severe as future spaceflight expands beyond low Earth orbit, where resupply is difficult or becomes impossible. These constraints thus convey an urgency to tailor medications for individual crewmembers and further examine appropriate dosing regimens.\n\nBACKGROUND: Precision Health is an exciting area of medicine focused on maintaining an individual’s health and performance through in-depth understanding of an individual’s unique clinical and environmental history, genetic makeup, and molecular profiles. This approach can be adapted to better predict, monitor, and address physiological responses to the spaceflight environment. A subset of this field is pharmacogenomics (PGX), the study of how the expressed genome impacts drug responses with the goal of prescribing the right dose of the right drug at the right time. Specifically, PGX testing provides valuable information on an individual’s precise allelic variations to guide physicians in making informed decisions on drug choice and dosing to avoid adverse events and maximize efficacy. The study goal was to identify which current space pharmacy drugs could be evaluated using PGX testing and to understand the potential impact on the health and wellness of the astronaut population. Additionally, we sought to evaluate clinically available FDA-approved PGX testing solutions to better understand its applicability.\n\nMETHODS: A complete list of drugs on the ISS was analyzed for risk and likelihood of drug failure and PGX actionability. This analysis encompassed both astronauts’ personal medications, including supplements and over the counter drugs (n=151) contained in the ISS medical accessory kit (IMAK), and ISS MedKit formulary medications (n=95). Duplicate medications and different formulations were removed, which resulted in a total of 157 drugs used in the subsequent analysis. A 5x5 risk assessment table was produced by examining the likelihood of drug failure compared to the consequence of drug failure (LxC). Likelihood of individual drug failure was defined by whether existing processes are sufficient to prevent ineffective treatment or impactful side effect events, as ranked from 1 (very low, can easily be prevented) to 5 (very high, cannot be prevented) during a Mars mission. In contrast, the consequence of drug failure was defined by impact to safety, schedule, cost, or technical criteria and ranked from 1 (very low) to 5 (very high). An assessment of PGX reference laboratories is currently underway to evaluate sample requirements, benefit analysis (cost vs. utility of allele variant analysis), relevance to inflight medication usage, quality of reporting in enabling clinical application, and ease of integration into electronic medical records.\n\nRESULTS: Risk assessments (LxC 5x5 table) indicated 128 medications were in the green zone where risk is acceptable, with the remaining 29 of the medications in the yellow or red zone driven predominantly due to drug failure or safety concerns. We found that current PGX testing results could impact 21% of the total medications in the ISS MedKit and IMAK; of these, 9 medications currently have direct clinically actionable guidance available. Results of the clinical PGX solution evaluations as related to these medications will be presented.\n\nCONCLUSION: PGX testing has demonstrated clear benefits in terrestrial medicine and clinical environments for the selection of proper medications, avoiding adverse drug reactions, and maximizing drug efficacy. We propose that similar benefits would be bestowed on the astronaut and commercial spaceflight passenger population by performing preemptive preflight PGX testing to reduce risk of mission failure due to ineffective or toxic medications, improve drug efficacy, and further open the door to countermeasure research. For example, PGX results could allow tailoring of specific medications at optimal doses more precisely to each individual astronaut, particularly in areas of space motion sickness, sleep aids, and analgesics. An additional benefit is that PGX results could provide information for better planning of the components of a space pharmacy for deep space missions to be more effective and efficient in the utilization of limited pharmaceutical resources. Finally, while PGX testing of the astronaut corps is not currently conducted, this approach could provide immediate impact in support of mission success by reducing risks, optimizing astronaut performance, and providing valuable insights into long-term astronaut health. Such advancements in clinical decision making are important next steps in building dynamic individual risk profiles for astronauts, increasing selection of the best treatment choice, and providing tailored countermeasures for individual crewmembers.

This article is from Genome Medicine , volume 5 . Abstract Implementation of pharmacogenomics (PGx) in clinical care can lead to improved drug efficacy and reduced adverse drug reactions. However, there has been a lag in adoption of PGx tests in clinical practice. This is due in part to a paucity of rigorous systems for translating published clinical and scientific data into standardized diagnostic tests with clear therapeutic recommendations. Here we describe the Pharmacogenomics Appraisal, Evidence Scoring and Interpretation System (PhAESIS), developed as part of the Coriell Personalized Medicine Collaborative research study, and its application to seven commonly prescribed drugs.

This article is from Indian Pacing and Electrophysiology Journal , volume 12 . Abstract Disorders of the cardiac rhythm are quite prevalent in clinical practice. Though the variability in drug response between individuals has been extensively studied, this information has not been widely used in clinical practice. Rapid advances in the field of pharmacogenomics have provided us with crucial insights on inter-individual genetic variability and its impact on drug metabolism and action. Technologies for faster and cheaper genetic testing and even personal genome sequencing would enable clinicians to optimize prescription based on the genetic makeup of the individual, which would open up new avenues in the area of personalized medicine. We have systematically looked at literature evidence on pharmacogenomics markers for anti-arrhythmic agents from the OpenPGx consortium collection and reason the applicability of genetics in the management of arrhythmia. We also discuss potential issues that need to be resolved before personalized pharmacogenomics becomes a reality in regular clinical practice.

This article is from Current Pharmacogenomics and Personalized Medicine , volume 11 . Abstract Publication of the research outputs is a vital step of the research processes and a gateway between the laboratory and the global society. Open Access is revolutionizing the dissemination of scientific ideas, particularly in the field of public health pharmacogenomics that examines the ways in which pharmacogenomics impacts health systems and services at a societal level, rather than a narrow bench to bedside model of translation science. This manuscript argues that despite some limitations and drawbacks, open access has profound ethical, political and societal implications especially on underdeveloped and developing countries, and that it provides opportunities for science to grow in these resource-limited countries, particularly in the era of a severe economic and financial crisis that is imposing cuts and restrictions to research.

This article is from Translational Psychiatry , volume 3 . Abstract Antidepressants are among the most widely prescribed medications, yet only 35–45% of patients achieve remission following an initial antidepressant trial. The financial burden of treatment failures in direct treatment costs, disability claims, decreased productivity, and missed work may, in part, derive from a mismatch between optimal and actual prescribed medications. The present 1 year blinded and retrospective study evaluated eight direct or indirect health care utilization measures for 96 patients with a DSM-IV-TR diagnosis of depressive or anxiety disorder. The eight measures were evaluated in relation to an interpretive pharmacogenomic test and reporting system, designed to predict antidepressant responses based on DNA variations in cytochrome P450 genes (CYP2D6, CYP2C19, CYP2C9 and CYP1A2), the serotonin transporter gene (SLC6A4) and the serotonin 2A receptor gene (5HTR2A). All subjects had been prescribed at least one of 26 commonly prescribed antidepressant or antipsychotic medications. Subjects whose medication regimen included a medication identified by the gene-based interpretive report as most problematic for that patient and are in the ‘red bin' (medication status of ‘use with caution and frequent monitoring'), had 69% more total health care visits, 67% more general medical visits, greater than three-fold more medical absence days, and greater than four-fold more disability claims than subjects taking drugs categorized by the report as in the green bin (‘use as directed') or yellow bin (‘use with caution'). There were no correlations between the number of medications taken and any of the eight healthcare utilization measures. These results demonstrate that retrospective psychiatric pharmacogenomic testing can identify past inappropriate medication selection, which led to increased healthcare utilization and cost.

This article is from Open Biology , volume 4 . Abstract In the post-genomic era, the rapid evolution of high-throughput genotyping technologies and the increased pace of production of genetic research data are continually prompting the development of appropriate informatics tools, systems and databases as we attempt to cope with the flood of incoming genetic information. Alongside new technologies that serve to enhance data connectivity, emerging information systems should contribute to the creation of a powerful knowledge environment for genotype-to-phenotype information in the context of translational medicine. In the area of pharmacogenomics and personalized medicine, it has become evident that database applications providing important information on the occurrence and consequences of gene variants involved in pharmacokinetics, pharmacodynamics, drug efficacy and drug toxicity will become an integral tool for researchers and medical practitioners alike. At the same time, two fundamental issues are inextricably linked to current developments, namely data sharing and data protection. Here, we discuss high-throughput and next-generation sequencing technology and its impact on pharmacogenomics research. In addition, we present advances and challenges in the field of pharmacogenomics information systems which have in turn triggered the development of an integrated electronic ‘pharmacogenomics assistant’. The system is designed to provide personalized drug recommendations based on linked genotype-to-phenotype pharmacogenomics data, as well as to support biomedical researchers in the identification of pharmacogenomics-related gene variants. The provisioned services are tuned in the framework of a single-access pharmacogenomics portal.

This article is from Frontiers in Genetics , volume 5 . Abstract Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized control trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed.

This article is from Frontiers in Genetics , volume 5 . Abstract Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized control trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed.

This article is from Genome Biology , volume 12 . Abstract Background: Recent studies have investigated the contribution of copy number variants (CNVs) to disease susceptibility in a multitude of complex disorders, including systemic lupus erythematosus, Crohn's disease, and various neurodevelopmental disorders. Relatively few CNV studies, however, have been conducted on pharmacologic phenotypes even though these structural variants are likely to play an important role. We developed a genome-wide method to identify CNVs that contribute to heterogeneity in drug response, focusing on drugs that are widely used in anticancer treatment regimens. Results: We conducted a comprehensive genome-wide study of CNVs from population-scale array-based and sequencing-based surveys by analyzing their effect on cellular sensitivity to platinating agents and topoisomerase II inhibitors. We identified extensive CNV regions associated with cellular sensitivity to functionally diverse chemotherapeutics, supporting the hypothesis that variation in copy number contributes to variation in drug response. Interestingly, although single nucleotide polymorphisms (SNPs) tag some of the CNVs associated with drug sensitivity, several of the most significant CNV-drug associations are independent of SNPs; consequently, they represent genetic variations that have not been previously interrogated by SNP studies of pharmacologic phenotypes. Conclusions: Our findings demonstrate that pharmacogenomic studies may greatly benefit from the study of CNVs as expression quantitative trait loci, thus contributing broadly to our understanding of the complex traits genetics of CNVs. We also extend our PACdb resource, a database that makes available to the scientific community relationships between genetic variation, gene expression, and sensitivity to various drugs in cell-based models.

This article is from Frontiers in Genetics , volume 5 . Abstract Introduction: Pharmacogenomics research has concentrated on variation in genes coding for drug metabolizing enzymes, transporters and nuclear receptors. However, variation affecting microRNA could also play a role in drug response. This project set out to investigate potential microRNA target sites in 11 genes and the extent of variation in the 3′-UTR of six selected genes; CYP1A2, CYP2B6, CYP2D6, CYP3A4, NR1I2, and UGT2B7.Methods: Fifteen microRNA target prediction algorithms were used to identify microRNAs predicted to regulate 11 genes. The 3′-UTR of the 6 genes which topped the list of potential microRNA targets was sequenced in 30 black South Africans. In addition, genetic variants within these genes were investigated for interference with mRNA-microRNA interactions. Potential effects of observed variants were determined using in silico prediction tools.Results: The 11 genes coding for DMEs, transporters and nuclear receptors were predicted to be targets of microRNAs with CYP2B6, NR1I2 (PXR), CYP3A4, and CYP1A2, interacting with the most microRNAs. The majority of identified genetic variants were predicted to interfere with microRNA regulation. For example, the variant, rs1054190C in NR1I2 was predicted to result in the presence of a binding site for the microRNA miR-1250-5p, while the variant rs1054191G was predicted to result in the absence of a recognition site for miR-371b-3p, miR-4258 and miR-4707-3p. Fifteen of the seventeen, novel variants occurred within microRNA target sequences.Conclusion: The 3′-UTR harbors variation that is likely to influence regulation of specific genes by microRNA. In silico prediction followed by functional validation could aid in decoding the contribution of variation in the 3′-UTR, to some unexplained heritability that affects drug response. Understanding the specific role of each microRNA may lead to identification of markers for targeted therapy and therefore improve personalized drug treatment.

This article is from Current Drug Metabolism , volume 15 . Abstract The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines.

This article is from PLoS ONE , volume 9 . Abstract The influence of self-reported “race/color”, geographical origin and genetic ancestry on the distribution of three functional CYP3A5 polymorphisms, their imputed haplotypes and inferred phenotypes was examined in 909 healthy, adult Brazilians, self-identified as White, Brown or Black (“race/color” categories of the Brazilian census). The cohort was genotyped for CYP3A5*3 (rs776746), CYP3A5*6 (rs10264272) and CYP3A5*7 (rs41303343), CYP3A5 haplotypes were imputed and CYP3A5 metabolizer phenotypes were inferred according to the number of defective CYP3A5 alleles. Estimates of the individual proportions of Amerindian, African and European ancestry were available for the entire cohort. Multinomial log-linear regression models were applied to infer the statistical association between the distribution of CYP3A5 alleles, haplotypes and phenotypes (response variables), and self-reported Color, geographical region and ancestry (explanatory variables). We found that Color per se or in combination with geographical region associates significantly with the distribution of CYP3A5 variant alleles and CYP3A5 metabolizer phenotypes, whereas geographical region per se influences the frequency distribution of CYP3A5 variant alleles. The odds of having the default CYP3A5*3 allele and the poor metabolizer phenotype increases continuously with the increase of European ancestry and decrease of African ancestry. The opposite trend is observed in relation to CYP3A5*6, CYP3A5*7, the default CYP3A5*1 allele, and both the extensive and intermediate phenotypes. No significant effect of Amerindian ancestry on the distribution of CYP3A5 alleles or phenotypes was observed. In conclusion, this study strongly supports the notion that the intrinsic heterogeneity of the Brazilian population must be acknowledged in the design and interpretation of pharmacogenomic studies, and dealt with as a continuous variable, rather than proportioned in arbitrary categories that do not capture the diversity of the population. The relevance of this work extrapolates the Brazilian borders, and extends to other admixed peoples of the Americas, with ancestral roots in Europe, Africa and the American continent.

This article is from PLoS ONE , volume 9 . Abstract The influence of self-reported “race/color”, geographical origin and genetic ancestry on the distribution of three functional CYP3A5 polymorphisms, their imputed haplotypes and inferred phenotypes was examined in 909 healthy, adult Brazilians, self-identified as White, Brown or Black (“race/color” categories of the Brazilian census). The cohort was genotyped for CYP3A5*3 (rs776746), CYP3A5*6 (rs10264272) and CYP3A5*7 (rs41303343), CYP3A5 haplotypes were imputed and CYP3A5 metabolizer phenotypes were inferred according to the number of defective CYP3A5 alleles. Estimates of the individual proportions of Amerindian, African and European ancestry were available for the entire cohort. Multinomial log-linear regression models were applied to infer the statistical association between the distribution of CYP3A5 alleles, haplotypes and phenotypes (response variables), and self-reported Color, geographical region and ancestry (explanatory variables). We found that Color per se or in combination with geographical region associates significantly with the distribution of CYP3A5 variant alleles and CYP3A5 metabolizer phenotypes, whereas geographical region per se influences the frequency distribution of CYP3A5 variant alleles. The odds of having the default CYP3A5*3 allele and the poor metabolizer phenotype increases continuously with the increase of European ancestry and decrease of African ancestry. The opposite trend is observed in relation to CYP3A5*6, CYP3A5*7, the default CYP3A5*1 allele, and both the extensive and intermediate phenotypes. No significant effect of Amerindian ancestry on the distribution of CYP3A5 alleles or phenotypes was observed. In conclusion, this study strongly supports the notion that the intrinsic heterogeneity of the Brazilian population must be acknowledged in the design and interpretation of pharmacogenomic studies, and dealt with as a continuous variable, rather than proportioned in arbitrary categories that do not capture the diversity of the population. The relevance of this work extrapolates the Brazilian borders, and extends to other admixed peoples of the Americas, with ancestral roots in Europe, Africa and the American continent.

This article is from Pharmacogenomics and Personalized Medicine , volume 7 . Abstract Chronic kidney disease (CKD) has shown an increasing prevalence in the last century. CKD encompasses a poor prognosis related to a remarkable number of comorbidities, and many patients suffer from this disease progression. Once the factors linked with CKD evolution are distinguished, it will be possible to provide and enhance a more intensive treatment to high-risk patients. In this review, we focus on the emerging markers that might be predictive or related to CKD progression physiopathology as well as those related to a different pattern of response to treatment, such as inhibitors of the renin–angiotensin system (including angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers; the vitamin D receptor agonist; salt sensitivity hypertension; and progressive kidney-disease markers with identified genetic polymorphisms). Candidate-gene association studies and genome-wide association studies have analyzed the genetic basis for common renal diseases, including CKD and related factors such as diabetes and hypertension. This review will, in brief, consider genotype-based pharmacotherapy, risk prediction, drug target recognition, and personalized treatments, and will mainly focus on findings in CKD patients. An improved understanding will smooth the progress of switching from classical clinical medicine to gene-based medicine.

This article is from Pharmacogenomics and Personalized Medicine , volume 7 . Abstract Chronic kidney disease (CKD) has shown an increasing prevalence in the last century. CKD encompasses a poor prognosis related to a remarkable number of comorbidities, and many patients suffer from this disease progression. Once the factors linked with CKD evolution are distinguished, it will be possible to provide and enhance a more intensive treatment to high-risk patients. In this review, we focus on the emerging markers that might be predictive or related to CKD progression physiopathology as well as those related to a different pattern of response to treatment, such as inhibitors of the renin–angiotensin system (including angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers; the vitamin D receptor agonist; salt sensitivity hypertension; and progressive kidney-disease markers with identified genetic polymorphisms). Candidate-gene association studies and genome-wide association studies have analyzed the genetic basis for common renal diseases, including CKD and related factors such as diabetes and hypertension. This review will, in brief, consider genotype-based pharmacotherapy, risk prediction, drug target recognition, and personalized treatments, and will mainly focus on findings in CKD patients. An improved understanding will smooth the progress of switching from classical clinical medicine to gene-based medicine.

Evaluation of the quality of online written pharmacogenomics patient health information.

This article is from The Pharmacogenomics Journal , volume 14 . Abstract Individual responses to growth hormone (GH) treatment are variable. Short-term generation of insulin-like growth factor-I (IGF-I) is recognized as a potential marker of sensitivity to GH treatment. This prospective, phase IV study used an integrated genomic analysis to identify markers associated with 1-month change in IGF-I (ΔIGF-I) following initiation of recombinant human (r-h)GH therapy in treatment-naïve children with GH deficiency (GHD) (n=166) or Turner syndrome (TS) (n=147). In both GHD and TS, polymorphisms in the cell-cycle regulator CDK4 were associated with 1-month ΔIGF-I (P

This article is from Molecular Cytogenetics , volume 7 . Abstract None

This article is from Molecular Cytogenetics , volume 7 . Abstract None

This article is from Genes , volume 5 . Abstract The completion of the human genome project 10 years ago was met with great optimism for improving drug therapy through personalized medicine approaches, with the anticipation that an era of genotype-guided patient prescribing was imminent. To some extent this has come to pass and a number of key pharmacogenomics markers of inter-individual drug response, for both safety and efficacy, have been identified and subsequently been adopted in clinical practice as pre-treatment genetic tests. However, the universal application of genetics in treatment guidance is still a long way off. This review will highlight important pharmacogenomic discoveries which have been facilitated by the human genome project and other milestone projects such as the International HapMap and 1000 genomes, and by the continued development of genotyping and sequencing technologies, including rapid point of care pre-treatment genetic testing. However, there are still many challenges to implementation for the many other reported biomarkers which continue to languish within the discovery phase. As technology advances over the next 10 years, and the costs fall, the field will see larger genetic data sets, including affordable whole genome sequences, which will, it is hoped, improve patient outcomes through better diagnostic, prognostic and predictive biomarkers.

Pharmacogenomics, the study of how an individual's genetic makeup influences their response to drugs, represents a frontier in personalized medicine. By identifying genetic variations that affect drug metabolism, efficacy, and safety, pharmacogenomics aims to optimize therapeutic strategies, minimize adverse reactions, and improve clinical outcomes. This review investigates the fundamental principles of genetics in drug response, highlighting key genes and variants implicated in pharmacogenomics, such as the cytochrome P450 enzyme family. It also examines clinical applications across diverse conditions, ethical challenges, and regulatory considerations. Despite significant advancements, barriers such as the complexity of genetic data interpretation, ethical concerns, and accessibility issues persist. Addressing these challenges through research, education, and policy will be crucial in integrating pharmacogenomics into routine clinical practice and ensuring equitable access to its benefits. 

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This article is from Frontiers in Genetics , volume 3 . Abstract The development of clinical practice recommendations or guidelines for the clinical use of biomarkers is an issue of great importance with regard to adverse drug reactions. The potential of pharmacogenomic biomarkers has been extensively investigated in recent years. However, several barriers to implementing the use of pharmacogenomics testing exist. We conducted a survey among members of the Spanish Societies of Pharmacology and Clinical Pharmacology to obtain information about the perception of such barriers and to compare the perceptions of participants about the relative importance of major gene/drug pairs. Of 11 potential barriers, the highest importance was attributed to lack of institutional support for pharmacogenomics testing, and to the issues related to the lack of guidelines. Of the proposed gene/drug pairs the highest importance was assigned to HLA-B/abacavir, UGT1A1/irinotecan, and CYP2D6/tamoxifen. In this perspective article, we compare the relative importance of 29 gene/drug pairs in the Spanish study with that of the same pairs in the American Society for Clinical Pharmacology and Therapeutics study, and we provide suggestions and areas of focus to develop a guide for clinical practice in pharmacogenomics testing.