HLA-B*51:01 is strongly associated with clindamycin-related cutaneous adverse drug reactions.

Pharmacogenomics J. 2016 Aug 16;

Authors: Yang Y, Chen S, Yang F, Zhang L, Alterovitz G, Zhu H, Xuan J, Yang X, Luo H, Mu J, He L, Luo X, Xing Q

Abstract
Clindamycin causes cutaneous adverse drug reactions (cADRs), sometimes with the mechanisms of pathogenicity or risk factors unknown. This study aims to assess whether HLA alleles are associated with clindamycin-related cADRs in the Han Chinese population. We performed an association study of 12 subjects with clindamycin-related cADRs, 279 controls and 26 clindamycin-tolerant subjects. Subjects who received clindamycin through intravenous drip were analyzed separately. Unbiased, in silico docking was conducted. We found 6 out of 12 clindamycin-induced cADR patients carried HLA-B*51:01, and all of them received clindamycin via intravenous drip (6/9). The carrier frequency of HLA-B*51:01 is significantly higher compared with the control group (P=0.0006; OR=9.731, 95% CI: 2.927-32.353) and the clindamycin-tolerant group (OR=24.000, 95% CI: 3.247-177.405). In silico docking showed clindamycin is potentially more stable inside HLA-B*51:01 protein. Our results suggested, for the first time, that HLA-B*51:01 is a risk allele for clindamycin-related cADRs in Han Chinese, especially when clindamycin is administered via intravenous drip.The Pharmacogenomics Journal advance online publication, 16 August 2016; doi:10.1038/tpj.2016.61.

PMID: 27527109 [PubMed - as supplied by publisher]

Adjuvant Therapy of Resected Non-small Cell Lung Cancer: can We Move Forward?

Curr Treat Options Oncol. 2016 Oct;17(10):54

Authors: Buffoni L, Vavalà T, Novello S

Abstract
OPINION STATEMENT: Twenty years ago, an individual patient data meta-analysis of eight cisplatin-based adjuvant chemotherapy (AC) studies in completely resected early stage non-small cell lung cancer (NSCLC) demonstrated a 13 % reduction of the risk of death favoring chemotherapy that was of borderline statistical significance (p = 0.08). This marginal benefit boosted a new generation of randomized trials to evaluate the role of modern platinum-based regimens in resectable stages of NSCLC and, although individual studies generated conflicting results, overall they contributed to confirm the role of AC which is now recommended for completely resected stage II and III NSCLC, mostly 4 cycles, while subset analyses suggested a benefit in patients with large IB tumors. Cisplatin-based therapy was the core regimen of those adjuvant clinical trials and even if a substitution with other platinum-derived was also suggested, mainly based on extrapolated data from studies in advanced disease, cisplatin was confirmed to be slightly superior to carboplatin and is still the drug of choice in the adjuvant setting. Currently, any attempt to improve efficacy of cisplatin-based chemotherapy through antiangiogenic drugs association or pharmacogenomics approaches have failed, while results of additional studies are eagerly awaited. In the context of promising targeted therapies, even if several randomized trials in the advanced setting evaluated tyrosine kinase inhibitors (TKis) versus platinum-based chemotherapy and showed impressive results, clinical experience with TKIs in the adjuvant setting is still limited and most of the trials have not required patients to be molecularly tested for the drug-specific molecular predictive factor. At the present time, the role of targeted agents as adjuvant approaches remains largely not investigated. Finally, with the negative experience of the use of vaccines in this setting, the integration of immunotherapy (mainly immunocheckpoint inhibitors) in platinum-based schedules has just started to be evaluated, representing a potential future clinical option, but still far from clinical practice.

PMID: 27523606 [PubMed - as supplied by publisher]

Pharmacometabolomics informs Pharmacogenomics.

Metabolomics. 2016 Jul;12(7)

Authors: Neavin D, Kaddurah-Daouk R, Weinshilboum R

Abstract
INTRODUCTION: The initial decades of the 21(st) century have witnessed striking technical advances that have made it possible to detect, identify and quantitatively measure large numbers of plasma or tissue metabolites. In parallel, similar advances have taken place in our ability to sequence DNA and RNA. Those advances have moved us beyond studies of single metabolites and single genetic polymorphisms to the study of hundreds or thousands of metabolites and millions of genomic variants in a single cell or subject. It is now possible to merge and integrate large data sets generated by the use of different "-omics" techniques to increase our understanding of the molecular basis for variation in disease risk and/or drug response phenotypes.
OBJECTIVES: This "Brief Review" will outline some of the challenges and opportunities associated with studies in which metabolomic data have been merged with genomics in an attempt to gain novel insight into mechanisms associated with variation in drug response phenotypes, with an emphasis on the application of a pharmacometabolomics-informed pharmacogenomic research strategy and with selected examples of the application of that strategy.
METHODS: Studies that used pharmacometabolomics to inform and guide pharmacogenomics were reviewed. Clinical studies that were used as the basis for pharmacometabolomics-informed pharmacogenomic studies, published in five independent manuscripts, are described briefly.
RESULTS: Within these five manuscripts, both pharmacokinetic and pharmacodynamic metabolomics approaches were used. Candidate gene and genome-wide approaches that were used in concert with these metabolomic data identified novel metabolite-gene relationships that were associated with drug response phenotypes in these pharmacometabolomics-informed pharmacogenomics studies.
CONCLUSION: This "Brief Review" outlines the emerging discipline of pharmacometabolomics-informed pharmacogenomics in which metabolic profiles are associated with both clinical phenotypes and genetic variants to identify novel genetic variants associated with drug response phenotypes based on metabolic profiles.

PMID: 27516730 [PubMed - as supplied by publisher]

Pharmacogenomics in Psychiatric Practice.

Clin Lab Med. 2016 Sep;36(3):507-23

Authors: El-Mallakh RS, Roberts RJ, El-Mallakh PL, Findlay LJ, Reynolds KK

Abstract
Pharmacogenomic testing in psychiatry is becoming an established clinical procedure. Several vendors provide clinical interpretation of combinatorial pharmacogenomic testing of gene variants that have documented predictive implications regarding either pharmacologic response or adverse effects in depression and other psychiatric conditions. Such gene profiles have demonstrated improvements in outcome in depression, and reduction of cost of care of patients with inadequate clinical response. Additionally, several new gene variants are being studied to predict specific response in individuals. Many of these genes have demonstrated a role in the pathophysiology of depression or specific depressive symptoms. This article reviews the current state-of-the-art application of psychiatric pharmacogenomics.

PMID: 27514465 [PubMed - in process]

Pharmacogenetics in Oral Antithrombotic Therapy.

Clin Lab Med. 2016 Sep;36(3):461-72

Authors: Maier CL, Duncan A, Hill CE

Abstract
Certain antithrombotic drugs exhibit high patient-to-patient variability that significantly impacts the safety and efficacy of therapy. Pharmacogenetics offers the possibility of tailoring drug treatment to patients based on individual genotypes, and this type of testing has been recommended for 2 oral antithrombotic agents, warfarin and clopidogrel, to influence use and guide dosing. Limited studies have identified polymorphisms that affect the metabolism and activity of newer oral antithrombotic drugs, without clear evidence of the clinical relevance of such polymorphisms. This article provides an overview of the current status of pharmacogenetics in oral antithrombotic therapy.

PMID: 27514462 [PubMed - in process]

Fundamentals of Pharmacogenetics in Personalized, Precision Medicine.

Clin Lab Med. 2016 Sep;36(3):447-59

Authors: Valdes R, Yin DT

Abstract
This article introduces fundamental principles of pharmacogenetics as applied to personalized and precision medicine. Pharmacogenetics establishes relationships between pharmacology and genetics by connecting phenotypes and genotypes in predicting the response of therapeutics in individual patients. We describe differences between precision and personalized medicine and relate principles of pharmacokinetics and pharmacodynamics to applications in laboratory medicine. We also review basic principles of pharmacogenetics, including its evolution, how it enables the practice of personalized therapeutics, and the role of the clinical laboratory. These fundamentals are a segue for understanding specific clinical applications of pharmacogenetics described in subsequent articles in this issue.

PMID: 27514461 [PubMed - in process]

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

BMC Med Genomics. 2016;9 Suppl 2:48

Authors: Liang Z, Huang JX, Zeng X, Zhang G

Abstract
BACKGROUND: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions.
METHODS: A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model.
RESULTS: There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models.
CONCLUSIONS: Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.

PMID: 27510822 [PubMed - in process]

Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).

Pharm Res. 2015 Dec;32(12):3785-802

Authors: Varma MV, Steyn SJ, Allerton C, El-Kattan AF

Abstract
Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework--Extended Clearance Classification System (ECCS)--which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A--metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B--transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2--metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A--renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B--transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4--renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin - Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10(-6) cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

PMID: 26155985 [PubMed - indexed for MEDLINE]

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis.

J Chem Inf Model. 2016 Aug 10;

Authors: Liu L, Tsompana M, Wang Y, Wu D, Zhu L, Zhu R

Abstract
Drug discovery and development is a costly and time-consuming process with a high risk for failure resulting primarily from a drug's associated clinical safety and efficacy potential. Identifying and eliminating inapt candidate drugs as early as possible is an effective way for reducing unnecessary costs, but limited analytical tools are currently available for this purpose. Recent growth in the area of toxicogenomics and pharmacogenomics has provided with a vast amount of drug expression microarray data. Web servers such as CMap and LTMap have used this information to evaluate drug toxicity and mechanisms of action independently, however their wider applicability has been limited by the lack of a combinatorial drug-safety type of analysis. Using available genome-wide drug transcriptional expression profiles, we developed the first web server for combinatorial evaluation of toxicity and efficacy of candidate drugs named "Connection Map for Compounds" (CMC). Using CMC, researchers can initially compare their query drug gene signatures with prebuilt gene profiles generated from two large-scale toxicogenomics databases, and subsequently perform a drug efficacy analysis for identification of known mechanisms of drug action or generation of new predictions. CMC provides a novel approach for drug repositioning and early evaluation in drug discovery with its unique combination of toxicity and efficacy analyses, expansibility of data and algorithms, and customization of reference gene profiles. CMC can be freely accessed at http://cadd.tongji.edu.cn/webserver/CMCbp.jsp.

PMID: 27508329 [PubMed - as supplied by publisher]

Clinical Interpretation of Genomic Variations.

Turk J Haematol. 2016 Aug 8;

Authors: Sayitoğlu M

Abstract
Novel high-throughput sequencing technologies generate large-scale genomic data and use extensively for disease mapping of monogenic and/or complex disorders, personalized treatment and pharmacogenomics. It is rapidly becoming a routine tool for diagnosis and molecular monitorization of the patients to evaluate the therapeutic efficiency. The next generation sequencing platforms generate huge amounts of genetic variation data and it remains a challenge to interpret the variations that identified. NGS data interpretation needs a close collaboration of bioinformaticians, clinicians and geneticist. There are several problems needs to draw attention such as generation of new algorithms for mapping and annotation, harmonization of the terminology, correct us of nomenclature, reference genome for different populations, rare disease variant databases and clinical reports.

PMID: 27507302 [PubMed - as supplied by publisher]

Irinotecan-induced toxicity pharmacogenetics: an umbrella review of systematic reviews and meta-analyses.

Pharmacogenomics J. 2016 Aug 9;

Authors: Campbell JM, Stephenson MD, Bateman E, Peters MD, Keefe DM, Bowen JM

Abstract
Irinotecan chemotherapy toxicities can be severe, and may result in treatment delay, morbidity and in some rare cases death. This systematic review of systematic reviews synthesises all meta-analyses on biomarkers for irinotecan toxicity across all genetic models for Asians, Caucasians, low dose, medium/high dose and regimens with and without fluorouracil. False-positive findings are a problem in pharmacogenetics, increasing the importance of systematic reviews. Four systematic reviews that investigated the effect of the polymorphisms UGT1A1*6 and/or*28 on neutropenia or diarrhoea toxicity were included. Both UGT1A1*6 and *28 were reliably demonstrated to be risk factors for irinotecan-induced neutropenia, with tests for both polymorphisms potentially being particularly useful in Asian cancer patients. UGT1A1*6 and *28 were also related to diarrhoea toxicity; however, at low doses of irinotecan there was evidence that UGT1A1*28 was not. In synthesising the best available evidence, this umbrella systematic review provides a novel reference for clinicians applying personalised medicine and identifies important research gaps.The Pharmacogenomics Journal advance online publication, 9 August 2016; doi:10.1038/tpj.2016.58.

PMID: 27503581 [PubMed - as supplied by publisher]

CXCR4 polymorphism predicts progression-free survival in metastatic colorectal cancer patients treated with first-line bevacizumab-based chemotherapy.

Pharmacogenomics J. 2016 Aug 9;

Authors: Matsusaka S, Cao S, Hanna DL, Sunakawa Y, Ueno M, Mizunuma N, Zhang W, Yang D, Ning Y, Stintzing S, Sebio A, Stremitzer S, Yamauchi S, Parekh A, Okazaki S, Berger MD, El-Khoueiry R, Mendez A, Ichikawa W, Loupakis F, Lenz HJ

Abstract
We analyzed associations between CXCR4/CXCL12 single-nucleotide polymorphisms and outcomes in metastatic colorectal cancer (mCRC) patients who underwent first-line bevacizumab-based chemotherapy. A total of 874 patients were included in this study: 144 treated with bevacizumab and FOLFOX or XELOX (training cohort), 653 treated with bevacizumab and FOLFIRI or FOLFOXIRI (validation cohort A or B) and 77 treated with cetuximab- and oxaliplatin-based regimens (control cohort). One CXCR4 polymorphism (rs2228014) and two CXCL12 polymorphisms (rs1801157 and rs3740085) were analyzed by PCR-based direct sequencing. Patients with a C/C genotype had a prolonged progression-free survival (PFS) compared with those with any T allele (P=0.030) in the training cohort. Similarly, patients with the C/C genotype had a superior PFS in the validation cohorts, but not in the control cohort. Our findings suggest that a common genetic variant, CXCR4 rs2228014, could predict PFS and may guide therapeutic decisions in mCRC patients receiving first-line bevacizumab-based chemotherapy.The Pharmacogenomics Journal advance online publication, 9 August 2016; doi:10.1038/tpj.2016.59.

PMID: 27503580 [PubMed - as supplied by publisher]

Clinical relevance of EMT and stem-like gene expression in circulating tumor cells of metastatic colorectal cancer patients.

Pharmacogenomics J. 2016 Aug 9;

Authors: Ning Y, Zhang W, Hanna DL, Yang D, Okazaki S, Berger MD, Miyamoto Y, Suenaga M, Schirripa M, El-Khoueiry A, Lenz HJ

Abstract
Using approved methods, circulating tumor cells (CTCs) are only isolated from blood in 30%-50% of metastatic colorectal cancer (mCRC) patients. We previously validated a technique to isolate circulating tumor cells (CTCs) in a cohort of mCRC patients by combining immunomagnetic enrichment of EpCAM(+)/CD45(-) cells with qRT-PCR amplification of CK20 and survivin expression. Here, we examined the prognostic utility of CTC epithelial-mesenchymal transition (EMT) and stem cell gene expression. An 8 ml blood sample was collected from 78 consecutive mCRC patients before treatment with investigational and standard chemotherapeutics. The mRNA expression of EMT (PI3Kα, Akt-2, Twist1) and stem cell (ALDH1) markers was measured. Associations between CTC gene expression and progression-free survival (PFS) and overall survival (OS) were determined using Cox regression models. Among patients without CK20 or survivin-expressing CTCs (n=17), 55% had expression of ALDH1, PI3Kα and/or Akt-2. Patients with positive CTC Akt-2 expression had a significantly shorter median PFS (3.0 versus 4.0 months) compared with those without CTC Akt-2 expression in univariable (hazard ratio (HR)=1.61; log-rank P=0.034) and multivariable analyses (HR=1.70; adjusted P=0.041). In univariable analysis, CTC ALDH1 expression was associated with shorter OS (10.0 versus 38.6 months; HR=2.04, P=0.021). Patients with CTCs expressing ALDH1, PI3Kα and/or Akt-2 had a significantly inferior PFS (3.0 versus 7.7 months; HR=1.88, P=0.015) and OS (10.0 versus 26.8+ months; HR=2.25, P=0.050) in univariable, but not multivariable, analysis.
CONCLUSIONS: CTC Akt-2 expression may serve as a clinically useful prognostic marker in mCRC patients and warrants further evaluation in prospective trials.The Pharmacogenomics Journal advance online publication, 9 August 2016; doi:10.1038/tpj.2016.62.

PMID: 27503579 [PubMed - as supplied by publisher]

Population differences in S-warfarin pharmacokinetics among African Americans, Asians and whites: their influence on pharmacogenetic dosing algorithms.

Pharmacogenomics J. 2016 Aug 9;

Authors: Kubo K, Ohara M, Tachikawa M, Cavallari LH, Lee MT, Wen MS, Scordo MG, Nutescu EA, Perera MA, Miyajima A, Kaneko N, Pengo V, Padrini R, Chen YT, Takahashi H

Abstract
Using population pharmacokinetic analysis (PPK), we attempted to identify predictors of S-warfarin clearance (CL(S)) and to clarify population differences in S-warfarin pharmacokinetics among a cohort of 378 African American, Asian and white patients. Significant predictors of CL(S) included clinical (age, body weight and sex) and genotypic (CYP2C9*2,*3 and *8) factors, as well as African American ethnicity, the median CL(S) being 30% lower in the latter than in Asians and whites (170 versus 243 and 250 ml h(-1), P<0.01). The plasma S-warfarin (Cp(S)) time courses following the genotype-based dosing algorithms simulated using the PPK estimates showed African Americans with CYP2C9*1/*1 and any of the VKORC1 genotypes would have an average Cp(S) at steady state 1.5-1.8 times higher than in Asians and whites. These results indicate warfarin dosing algorithms should be evaluated in each respective ethnic population. Further study of a large African American cohort will be necessary to confirm the present findings.The Pharmacogenomics Journal advance online publication, 9 August 2016; doi:10.1038/tpj.2016.57.

PMID: 27503578 [PubMed - as supplied by publisher]

Pharmacogenomics for leukemia treatment.

Rinsho Ketsueki. 2016 Jul;57(7):910-8

Authors: Tanaka Y

Abstract
The pharmacokinetics and pharmacodynamics of therapeutic drugs can greatly vary among individuals. For example, it is sometimes necessary to alter the treatment of childhood acute lymphoblastic leukemia from the standard protocol. Genetic variation is one important factor, which can exert a wide range of effects on sensitivities and responses to therapeutic agents. Thiopurine S-methyl transferase (TPMT) is a useful test for predicting 6-mercaptopurine (6-MP) sensitivity in Caucasians. However, it is not effective for predicting the 6-MP therapeutic responses of Japanese patients because the frequency of TPMT deficiency is lower in the Japanese population (approximately 1% versus approximately 10% in Caucasians). Recently, NUDT15 polymorphisms have been reported to be predictive factors contributing to responsiveness to thiopurine therapy in Asians. The associations between genetic variants and therapeutic responses have been reported in Western countries. However, questions remain about whether results studying other races are applicable to Japanese due to differences in genetic variant frequencies among races. To provide personalized therapy based on genetic factors, we need to ascertain the relationships between genetic variants and therapeutic responses in Japanese childhood acute lymphoblastic leukemia cases.

PMID: 27498738 [PubMed - in process]

Role of Proteomics in the Development of Personalized Medicine.

Adv Protein Chem Struct Biol. 2016;102:41-52

Authors: Jain KK

Abstract
Advances in proteomic technologies have made import contribution to the development of personalized medicine by facilitating detection of protein biomarkers, proteomics-based molecular diagnostics, as well as protein biochips and pharmacoproteomics. Application of nanobiotechnology in proteomics, nanoproteomics, has further enhanced applications in personalized medicine. Proteomics-based molecular diagnostics will have an important role in the diagnosis of certain conditions and understanding the pathomechanism of disease. Proteomics will be a good bridge between diagnostics and therapeutics; the integration of these will be important for advancing personalized medicine. Use of proteomic biomarkers and combination of pharmacoproteomics with pharmacogenomics will enable stratification of clinical trials and improve monitoring of patients for development of personalized therapies. Proteomics is an important component of several interacting technologies used for development of personalized medicine, which is depicted graphically. Finally, cancer is a good example of applications of proteomic technologies for personalized management of cancer.

PMID: 26827601 [PubMed - indexed for MEDLINE]

Surveying Recent Themes in Translational Bioinformatics: Big Data in EHRs, Omics for Drugs, and Personal Genomics.

Yearb Med Inform. 2014;9:199-205

Authors: Denny JC

Abstract
OBJECTIVE: To provide a survey of recent progress in the use of large-scale biologic data to impact clinical care, and the impact the reuse of electronic health record data has made in genomic discovery.
METHOD: Survey of key themes in translational bioinformatics, primarily from 2012 and 2013.
RESULT: This survey focuses on four major themes: the growing use of Electronic Health Records (EHRs) as a source for genomic discovery, adoption of genomics and pharmacogenomics in clinical practice, the possible use of genomic technologies for drug repurposing, and the use of personal genomics to guide care.
CONCLUSION: Reuse of abundant clinical data for research is speeding discovery, and implementation of genomic data into clinical medicine is impacting care with new classes of data rarely used previously in medicine.

PMID: 25123743 [PubMed - indexed for MEDLINE]

Significance of pharmacogenetics and pharmacogenomics research in current medical practice.

Curr Drug Metab. 2016 Aug 4;

Authors: Prakash S, Agrawal S

Abstract
Human genome sequencing highlights the involvement of genetic variation towards differential risk of human diseases, presence of different phenotypes, and response to pharmacological elements. This brings the field of personalized medicine to forefront in the era of modern health care. Numerous recent approaches have shown that how variation in the genome at single nucleotide level can be used in pharmacological research. The two broad aspects that deal with pharmacological research are pharmacogenetics and pharmacogenomics. This review encompasses how these variations have created the basis of pharmacogenetics and pharmacogenomics research and important milestones accomplished in these two fields in different diseases. It further discusses at length their importance in disease diagnosis, response of drugs, and various treatment modalities on the basis of genetic determinants.

PMID: 27494310 [PubMed - as supplied by publisher]

Clinical Interpretation of Variants from Next - Generation Sequencing: The 2016 Scientific Meeting of the Human Genome Variation Society.

Hum Mutat. 2016 Aug 5;

Authors: Oetting WS, Brookes AJ, Béroud C, Taschner PE

Abstract
The 2016 scientific meeting of the Human Genome Variation Society (HGVS; http://www.hgvs.org) was held on the 20(th) of May in Barcelona, Spain, with the theme of "Clinical Interpretation of Variants from Next - Generation Sequencing". The meeting was opened by William S. Oetting, of the University of Minnesota, United States. "Precision medicine" is the latest buzz words in health care, both in the literature and in government initiatives. Pharmacogenomics is one area where next-generation sequencing (NGS) will have an impact, but there are some issues that need to be addressed. This article is protected by copyright. All rights reserved.

PMID: 27492570 [PubMed - as supplied by publisher]

Montelukast, current indications and prospective future applications.

Expert Rev Respir Med. 2016 Aug 2;:1-14

Authors: Kittana N, Hattab S, Ziyadeh-Isleem A, Jaradat N, Zaid AN

Abstract
INTRODUCTION: Montelukast is recommended for the treatment of asthma, exercise -induced bronchospasm and allergic rhinitis. Several trials demonstrated potential therapeutic effects in other respiratory conditions, and different animal-model-based studies explored potential pharmacological actions in non-respiratory conditions.
AREAS COVERED: Clinical investigations on the pharmacotherapeutic effects of montelukast, in addition to in-vivo studies on animal models of non-respiratory diseases. The data discussed in this review were mainly obtained from clinical randomized trials, real-life studies, and studies based on animal models as approve of concept. As a condition, all of the discussed articles were published in journals cited by Pubmed. Expert commentary: The current clinical data are in favor of montelukast use in the management of chronic asthma as an add-on or alternative therapy to the inhaled corticosteroids. Further clinical trials are required to confirm the effectiveness and feasibility of montelukast for the treatment of conditions other than the current clinical indications.

PMID: 27485393 [PubMed - as supplied by publisher]