Field Of Pharmacogenomics Research Articles

The implementation of pharmacogenomics into UK general practice: a qualitative study exploring barriers, challenges and opportunities

Pharmacogenomics describes interpatient genetic variability in drug responses. Information based on whole genome sequencing will soon open up the field of pharmacogenomics and facilitate the use of genomic information relating to drug metabolism and drug responses. We undertook a qualitative study, aiming to explore the potential barriers, opportunities and challenges facing the implementation of pharmacogenomics into primary care. Semi-structured interviews were undertaken with 18 clinical participants (16 GPs and 2 other clinicians). All interviews were recorded and transcribed verbatim. Using a thematic analysis approach, data items were coded, ordered and themes constructed. Most participants were aged 55–60 years and worked as part-time clinical GPs with other clearly defined roles. The emerging themes covered several areas of concern, including the following: the utility of pharmacogenomics and the value of introducing such testing into primary care; how to educate the primary care workforce and ‘mainstream’ pharmacogenomics; the ethical, legal and social aspects of pharmacogenomics and its impact on patients; and potential impacts on the healthcare system particularly around economics and informatics. Most participants had concerns about pharmacogenomics and felt that there were a number of barriers and challenges to its implementation into routine primary care. Most striking were their concerns around the cost-effectiveness of using pharmacogenomics in primary care. At the same time most recognised the increasing availability of direct-to-consumer testing, and felt that this would drive the need to understand the ethical and social implications of using genomic information in primary care. This study has raised important issues that need to be considered when planning the implementation of pharmacogenomics into clinical practice. Prior to the implementation of genomic testing into day-to-day practice in UK primary care, it is important that considerations around education, cost-effectiveness and informatics are addressed, as well as the impact on patients.

Pharmacogenomics: an Update for Child and Adolescent Psychiatry.

This paper aims to acquaint child and adolescent psychiatrists with the field of pharmacogenomics (PGX) and review the most up-to-date evidence-based practices to guide the application of this field in clinical care. Despite much research being done in this area, the field of PGX continues to yield controversial findings. In the adult world, studies have focused on the impact of combinatorial gene panels that guide medication selection by providing reports that estimate the impact of multiple pharmacodynamic and pharmacokinetic genes, but to date, these have not been directly examined in younger patient populations. Pharmacokinetic genes, CYP2D6 and CYP2C19, and hypersensitivity genes, HLA-A and HLA-B, have the strongest evidence base for application to pharmacotherapy in children. Although the field is evolving, and the evidence is mixed, there may be a role for PGX testing in children to help guide dosing and monitoring strategies. However, evidence-based medicine, rather than PGX testing, continues to play the lead role in guiding medication selection in pediatric psychopharmacology.

Genotype and Phenotype Concordance for Pharmacogenetic Tests Through Proficiency Survey Testing.

As pharmacogenetic testing is incorporated into routine care, it is essential for laboratories to provide accurate and consistent results. Certified laboratories must successfully complete proficiency testing. To understand and examine trends in participation and performance of laboratories participating in the College of American Pathologists pharmacogenetic proficiency testing surveys. Results from College of American Pathologists pharmacogenetic proficiency testing challenges from 2012 through 2017 were reviewed for concordance with expected genotype and phenotype for each sample (intended responses). Laboratories correctly reported results for 96.7% to 100% of samples with no variants. Excluding CYP2D6, laboratories correctly detected and reported variant alleles for each gene (93.7%-99.2% correct). CYP2D6 showed lower concordance, with 83.1% of laboratories reporting the intended genotype across all samples; however, in many cases, the laboratories that did not report a variant allele did not test for that allele. Among laboratories reporting the intended genotype, most successfully reported the intended phenotype (85.9%-99.0%). Although laboratories are generally performing well, there is room for additional improvement, particularly for challenging genes, such as CYP2D6. Efforts in the field of pharmacogenomics to recommend alleles that should be included in clinical tests, identify reference materials, and standardize translation from genotype to phenotype may address some of the remaining variability in results.

Genetic Influence on Efficacy of Pharmacotherapy for Pediatric Attention-Deficit/Hyperactivity Disorder: Overview and Current Status of Research.

Multiple stimulant and non-stimulant medications are approved for the treatment of attention-deficit/hyperactivity disorder (ADHD), one of the most prevalent childhood neurodevelopmental disorders. Choosing among the available agents and determining the most effective ADHD medication for a given child can be a time-consuming process due to the high inter-individual variability in treatment efficacy. As a result, there is growing interest in identifying predictors of ADHD medication response in children through the burgeoning field of pharmacogenomics. This article reviews childhood ADHD pharmacogenomics efficacy studies published during the last decade (2009-2019), which have largely focused on pharmacodynamic candidate gene investigations of methylphenidate and atomoxetine response, with a smaller number investigating pharmacokinetic candidate genes and genome-wide approaches. Findings from studies which have advanced the field of ADHD pharmacogenomics through investigation of meta-analytic approaches and gene-gene interactions are also overviewed. Despite recent progress, no one genetic variant or currently available pharmacogenomics test has demonstrated clinical utility in pinpointing the optimal ADHD medication for a given individual patient, highlighting the need for further investigation.

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with “normal” (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, “sensitive”), and high (i.e., ≥49 mg/week, “resistant”) dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with “normal” dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the “sensitive” group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with “normal,” “sensitive,” and “resistant” to warfarin were obtained when compared to other populations and previous statistical models.

Reverse Vaccinology and Its Applications.

The application of the fields of pharmacogenomics and pharmacogenetics to vaccine design, profoundly combined with bioinformatics, has been recently termed "vaccinomics." The enormous amount of information generated by whole genome sequencing projects and the rise of bioinformatics has triggered the birth of a new era of vaccine research and development, leading to a "third generation" of vaccines, which are based on the application of vaccinomics science to vaccinology. The first example of such an approach is reverse vaccinology. Reverse vaccinology reduces the period of vaccine target detection and evaluation to 1-2years. This approach targets the genomic sequence and predicts those antigens that are most likely to be vaccine candidates. This approach allows not only the identification of all the antigens obtained by the previous methods but also the discovery of new antigens that work on a totally different paradigm. Hence this method helps in the discovery of novel mechanisms of immune intervention. Epitope-based immune-derived vaccines (IDV) are generally considered to be safe when compared to other vectored or attenuated live vaccines. Epitope-based IDV may also provide essential T-cell help for antibody-directed vaccines. Such vaccines may have a significant advantage over earlier vaccine design approaches, as the cautious assortment of the components may diminish.

Unveiling the guidance heterogeneity for genome-informed drug treatment interventions among regulatory bodies and research consortia

Pharmacogenomics and personalized medicine interventions hold promise to optimize drug treatment modalities and hence, improve the quality of life of the patients by minimizing the occurrence of adverse drug reactions and/or maximizing drug treatment efficacy. To this end, proper guidance for accurately prescribing the correct drug at the right dose is empowered by major regulatory bodies, namely the U.S. Food and Drug Administration (FDA) and the European Medicine Agency (EMA), and well-recognized research consortia, like the Clinical Pharmacogenetics Implementation Consortium (CPIC), that propose therapeutic recommendations after the thorough evaluation of the existing scientific evidence base. In this context, the consistency of these recommendations is crucial for smoothly integrating pharmacogenomics into the clinic. Here, we collected all of the important and clinically actionable pharmacogenomics information provided by the aforementioned renowned sources and documented it in order to assess potential similarities and, most importantly, differences. Our data show that the level of concordance regarding the guidance provided for the same drug-gene association pairs varies significantly, despite the fact that it all derives from a single evidence base. In particular, apart from the expected similarities in a number of association pairs, especially the ones related to cancer genomics, there are still major discrepancies that create confusion as to which guidance should be followed in order to properly inform drug prescribing. This regulatory deficiency calls for the fruitful engagement of the regulatory agencies involved with the contribution of other experts engaged in the field of pharmacogenomics in an effort to harmonize the existing arsenal of guidance for genome-informed drug prescription. The achievement of harmonization would in turn expedite bringing personalized medicine closer to clinical fruition.

Pharmacogenomics In Pharmacy Practice: Current Perspectives.

Pharmacogenomics (i.e., the application of genetic information in predicting an individual’s response to drug therapy) plays an increasingly important role in drug development and decision-making regarding precision medicine. This has been shown to reduce the risk of adverse events and improve patient health-care outcomes through targeted therapies and dosing. As the field of pharmacogenomics rapidly evolves, the role of pharmacists in the education, implementation, and research applications of pharmacogenomics is becoming increasingly recognized. This paper aims to provide an overview and current perspectives of pharmacogenomics in contemporary clinical pharmacy practice and to discuss the future directions on advancing pharmacogenomics education, application, and research in pharmacy practice.

4th ESPT Conference: pharmacogenomics and personalized medicine- research progress and clinical implementation.

The Fourth European Society of Pharmacogenomics and Personalized Therapy biennial conference was organized in collaboration with the Italian Society of Personalized Medicine (SIMeP) and was held at Benedictine Monastery of San Nicolò l'Arena in Catania, Sicily (Italy) on 4-7 October 2017. The congress addressed the research progress and clinical implementation in pharmacogenomics and personalized medicine. The Fourth European Society of Pharmacogenomics and Personalized Therapy congress brought together leading international scientists and healthcare professionals actively working in the fields of pharmacogenomics and personalized therapy. Altogether, 25 speakers in 15 session comprehensively covered broad spectrum of pharmacogenetics and pharmacogenomics research, clinical applications in different clinical disciplines attended by 270 delegates.

Creating reproducible pharmacogenomic analysis pipelines

The field of pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community.

The Emerging Focus on Pharmacogenomics

Drugs are designed to treat medical conditions for the general population. Idiosyncratic reactions to drugs are determined by the individual’s respective genetic variations that direct effectiveness and side effects. Adverse drug reactions rank within the top ten leading causes of death in the developed world. The field of pharmacogenomics has advanced in the last fifty years, picking up significant momentum with recent biotechnological developments that allow scientists to investigate the human genome and provide individualized drug therapy that will increase the efficacy of drugs and decrease the incidence of adverse drug reactions. Pharmacogenomics has reached a milestone in making personalized medicine accessible and effective. The medical community shares this responsibility for the emerging focus on pharmacogenomics with regulatory agencies and bioinformatics specialists as they struggle to streamline vast libraries of information and reconcile public and regulatory approval on this critical path to the next level of health care.

Pharmacogenetics of opioids: a narrative review.

Advances in the field of pharmacogenomics have resulted in the discovery of some important single-nucleotide polymorphisms which are found to be associated with opioid dose variability. This, to a large extent, explains genetic variability in the analgesic dose of opioids. These polymorphisms are found in various areas relevant to pain perception, including the nociceptive and antinociceptive pathways, drug receptors, drug-metabolising enzymes and drug efflux molecules. An in-depth knowledge of single-nucleotide polymorphisms can help clinicians to address interindividual variability in opioid dosing and requirements. In the era of precision medicine, these genetic markers can also help us to design prognostic tools to accurately predict the analgesic dose of opioids.

Efficacy of antiepileptic drugs in the era of pharmacogenomics: A focus on childhood.

In recent years advances in the field of pharmacogenomics have expanded the concept for more individualized treatments. Our aim is to provide literature data about the relationship between genetic polymorphisms and efficacy of antiepileptic drugs in children. Pubmed was used as the main medical database source. Only original research papers were considered. No year-of-publication restriction was placed. Quality of evidence was assessed according to American Academy of Neurology guidelines. A total of 12 cross-sectional and case-control studies fulfilled our selection criteria. ABCB1 gene was associated with drug responsiveness in 2 out of 6 studies and ABCC2 gene in 1 out of 1 studies. SCN1A gene was also associated with seizure control in 4 out of 5 studies. Cytochrome P450 genes were found to significantly affect drug responsiveness in 2 out of 4 studies, while polymorphisms of uridinediphosphateglucuronosyltransferaseUGT2B7 gene predisposed to drug-resistance in 1 out of 2 studies. Variability in genes coding for sodium channels, drug transporters and cytochrome P450 enzymes can have a significant impact on response to antiepileptic drugs. Larger prospective studies with better stratification of samples are needed to shed light on these associations.

Encircling the regions of the pharmacogenomic landscape that determine drug response

BackgroundThe integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.MethodsTo simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.ResultsWe apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.ConclusionsNetwork biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

Pharmacogenomics in dermatology: tools for understanding gene-drug associations.

Pharmacogenomics aims to associate human genetic variability with differences in drug phenotypes in order to tailor drug treatment to individual patients. The massive amount of genetic data generated from large cohorts of patients with variable drug phenotypes have led to advances in this field. Understanding the application of pharmacogenomics in dermatology could inform clinical practice and provide insight for future research. The Pharmacogenomics Knowledge Base and the Clinical Pharmacogenetics Implementation Consortium are among the resources to help clinicians and researchers navigate the many gene-drug associations that have already been discovered. The implementation of clinical pharmacogenomics within health care systems remains an area of ongoing development. This review provides an introduction to the field of pharmacogenomics and to current pharmacogenomics resources using examples of gene-drug associations relevant to the field of dermatology.

Assessment of provider-perceived barriers to clinical use of pharmacogenomics during participation in an institutional implementation study.

The objective of this study was to study provider attitudes of and perceived barriers to the clinical use of pharmacogenomics before and during participation in an implementation program. From 2012 to 2017, providers were recruited. After completing semistructured interviews (SSIs) about pharmacogenomics, providers received training on and access to a clinical decision support tool housing patient-specific pharmacogenomic results. Thematic analysis of SSI was conducted (inter-rater reliability κ≥0.75). Providers also completed surveys before and during study participation, and provider-perceived barriers to pharmacogenomic implementation were analyzed. Seven themes emerged from the SSI (listed from most frequent to least): decision-making, concerns with pharmacogenomic adoption, outcome expectancy, provider knowledge of pharmacogenomics, patient attitudes, individualized treatment, and provider interest in pharmacogenomics. Although there was prestudy enthusiasm among all providers, concerns with clinical utility, time, results accession, and knowledge of pharmacogenomics were frequently stated at baseline. Despite this, adoption of pharmacogenomics was robust, as patient-specific results were accessed at 64% of visits, and medication changes were influenced by provided pharmacogenomic information 42% of the time. Providers reported they had enough time to evaluate the information and the results were easily understood on 74 and 98% of surveys, respectively. Nevertheless, providers consistently felt there was insufficient pharmacogenomic information for most drugs they prescribed and clear guidelines for using pharmacogenomic information were lacking. Despite initial concerns about adequate time and knowledge for adoption, providers frequently utilized pharmacogenomic results. Provider-perceived barriers to wider use included lack of clear guidelines and evidence for most drugs, highlighting important considerations for the field of pharmacogenomics.

Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer.

Following the completion of the Human Genome Project in 2003, research in oncology has progressively focused on the sequencing of cancer genomes, with the aim of better understanding the genetic basis of oncogenesis and identifying actionable alterations. The development of next-generation-sequencing (NGS) techniques, commercially available since 2006, allowed for a cost- and time-effective sequencing of tumor DNA, leading to a "genomic era" of cancer research and treatment. NGS provided a significant step forward in Personalized Medicine (PM) by enabling the detection of somatic driver mutations, resistance mechanisms, quantification of mutational burden, germline mutations which settled the foundation of a new approach in cancer care. In this chapter we discuss the history, available techniques and applications of NGS in oncology, with a particular referral to the PM approach and the emerging role of the research field of pharmacogenomics.

Φαρμακογονιδιωματική: Ηθικά διλήμματα

In the present article I will depict some of the emerging controversies that are expected to be of serious concern in the developing field of pharmacogenomics. Pharmacogenomics is the branch of genetics concerned with determining the likely response of an individual to therapeutic medications. The first upcoming issue is the voluntary agreement of the patients, concerning the research of their genome, that is also reflected in their relatives, as well as in their wider social group (s). Secondly, the great financial costs of pharmacogeonomic therapies is most likely to lead to new social inequalities or upsurge the existing ones in commodity of health. Furthermore, this will possibly lead to more group minorities, based on the rarity of some alleles among individuals and their following response to the applied medication. As for cognitive functions, pharmacogenomics seems to be a great challenge for the enhancement of both cognitive dysregulation in patients and intellectual ability in healthy individuals. Consequently, this will lead to an unbridgeable gap between the users and nonusers of newer medications regarding their intellectual abilities, that are unachievable by normal training of physical abilities. Last but not least, l will refer to whether or not genetic data regard strictly confidential personal data, not allowed to be published, despite of possible social benefit and to what extent their carrier has the right of ignoring it.

The Clinical Utility of Pharmacogenomics Testing in Assessing Tyrosine Kinase Inhibitor Therapy, Intolerance and Responses in Patients with Chronic Myelogenous Leukemia

Background: Chronic myelogenous leukemia (CML) is a BCR-ABL1 driven myeloid neoplasm, with excellent response rates to tyrosine kinase inhibitors (TKI). TKI such as imatinib (IM), dasatinib (DAS), nilotinib (NIL), bosutinib (BOS), and ponatinib (PON) currently US FDA approved, have dramatically changed survival outcomes. That being said, two main challenges exist; suboptimal responses and TKI intolerance. The reason why some patients tolerate/respond to TKI better than others is unknown, but is potentially explainable based on drug metabolism. The field of pharmacogenomics (PGX) is rapidly evolving, with commercial panel's rapidly assessing metabolic pathways such as cytochrome P450 (CYP), UGTA1A, p-glycoprotein/ABCB1. We carried out this study using a 22 gene-PGX panel to assess potential causes of TKI related intolerance and suboptimal responses in compliant patients.Methods: Twenty-nine Mayo Clinic patients with CML were prospectively recruited after informed consent. Buccal swabs were utilized for PGX testing to assess variations/polymorphisms involving CYP3A4/5, 1A2, 2C9, UGT1A1, amongst others (www.Oneome.com). Three groups of patients were recruited, those with i) TKI intolerance, ii) suboptimal responses by ELN criteria, iii) and newly diagnosed cases, to assess if PGX testing could explain TKI intolerance or resistance, or alter choice of therapy in newly diagnosed cases. A pharmacology review was obtained to interpret reports in all cases.Results:29 patients with CML were prospectively enrolled, median age 64 years, 15 (52%) male. At last follow up (median 21 months), 3 (10%) deaths and no disease progressions were documented. 82% received first line IM, whereas 18% received first line DAS. Major metabolic pathways assessed for IM included, CYP3A4 and 5, while minor pathways included 2C19, 2C9 and 2D6. Majority (83%) of IM patients had normal major pathways with 17% being intermediate-normal. With regards to the minor pathways, 25% were rapid metabolizers for 2C19, whereas 29% were intermediate and 4% poor metabolizers for 2C9 and 50% and 16% were intermediate and poor metabolizers for 2D6. In all IM treated patients PGX testing did not completely explain intolerance or resistance and did not change clinical decision making. Front line DAS was used in 18% and the major pathway assessed was CYP 3A4, with 80% being normal and 20% being intermediate metabolizers. Once again PGX testing did not completely explain intolerance/resistance and no therapeutic changes were based on PGX analysis.Seventeen (59%) patients received second line TKI therapy (50% each for intolerance and resistance), with DAS (58%) and NIL (29%), being the two most common. Once again for DAS, 90% had normal 3A4 metabolism while 10% were intermediate and for NIL 80% were normal and 20% intermediate. Only one of 2 NIL treated patients with UGTA1A polymorphisms developed indirect hyperbilirubinemia (grade 1). In the second line setting, PGX testing did not completely explain or alter treatment decisions. BOS was used as a third line agent in 3 (>10%) patients, 2 who remained refractory and one with intolerance, and all three were normal CYP3A4 metabolizers. None of these patients developed diarrhea a common side effect of BOS. PON was used in one patient with intermediate CYP3A4 metabolism as a 5th line agent and the patient continues to have refractory disease with no thrombotic events. In all cases, no changes were made to concomitant medications based on PGX testing.Conclusion: In conclusion, while PGX testing has rapidly evolved and become commercially available, in the context of TKI therapy for CML, while it offers useful insights on minor metabolic derangements and side effects like NIL induced hyperbilirubinemia, in our study, currently limited by a small sample size, it did not completely explain TKI intolerance or resistance. Recruitment of additional patients (n=100) and correlations with plasma drug levels are currently ongoing. DisclosuresAl-Kali:Novartis: Research Funding. Stewart:Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding; Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy.

Cardiovascular Pharmacogenomics: Does It Matter If You're Black or White?

Race and ancestry have long been associated with differential risk and outcomes to disease as well as responses to medications. These differences in drug response are multifactorial with some portion associated with genomic variation. The field of pharmacogenomics aims to predict drug response in patients prior to medication administration and to uncover the biological underpinnings of drug response. The field of human genetics has long recognized that genetic variation differs in frequency between ancestral populations, with some single nucleotide polymorphisms found solely in one population. Thus far, most pharmacogenomic studies have focused on individuals of European and East Asian ancestry, resulting in a substantial disparity in the clinical utility of genetic prediction for drug response in US minority populations. In this review, we discuss the genetic factors that underlie variability to drug response and known pharmacogenomic associations and how these differ between populations, with an emphasis on the current knowledge in cardiovascular pharmacogenomics.