scholarly journals Personalized medicine in thrombosis: back to the future

Blood ◽  
2016 ◽  
Vol 127 (22) ◽  
pp. 2665-2671 ◽  
Author(s):  
Srikanth Nagalla ◽  
Paul F. Bray
Keyword(s):  
Personalized Medicine ◽  
Disease Risk ◽  
Platelet Reactivity ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Personal Data ◽  
Value Added ◽  
Antiplatelet Agents ◽  
Small Effect Size ◽  
The Individual

Abstract Most physicians believe they practiced personalized medicine prior to the genomics era that followed the sequencing of the human genome. The focus of personalized medicine has been primarily genomic medicine, wherein it is hoped that the nucleotide dissimilarities among different individuals would provide clinicians with more precise understanding of physiology, more refined diagnoses, better disease risk assessment, earlier detection and monitoring, and tailored treatments to the individual patient. However, to date, the “genomic bench” has not worked itself to the clinical thrombosis bedside. In fact, traditional plasma-based hemostasis-thrombosis laboratory testing, by assessing functional pathways of coagulation, may better help manage venous thrombotic disease than a single DNA variant with a small effect size. There are some new and exciting discoveries in the genetics of platelet reactivity pertaining to atherothrombotic disease. Despite a plethora of genetic/genomic data on platelet reactivity, there are relatively little actionable pharmacogenetic data with antiplatelet agents. Nevertheless, it is crucial for genome-wide DNA/RNA sequencing to continue in research settings for causal gene discovery, pharmacogenetic purposes, and gene-gene and gene-environment interactions. The potential of genomics to advance medicine will require integration of personal data that are obtained in the patient history: environmental exposures, diet, social data, etc. Furthermore, without the ritual of obtaining this information, we will have depersonalized medicine, which lacks the precision needed for the research required to eventually incorporate genomics into routine, optimal, and value-added clinical care.

scholarly journals Accurate genetic profiling of anthropometric traits using a big data approach

2015 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
John A. Woolliams ◽  
Albert Tenesa
Keyword(s):  
Disease Management ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Genomic Medicine ◽  
Full Potential ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Starting Point ◽  
The Individual

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions in White non-British individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.

scholarly journals Genomic research perspectives in Kazakhstan

2014 ◽  
Vol 2 ◽  
Author(s):  
Ainur Akilzhanova
Keyword(s):  
Personalized Medicine ◽  
Population Genomics ◽  
Cancer Genomics ◽  
Clinical Symptoms ◽  
Disease Risk ◽  
Genomic Medicine ◽  
Life Sciences ◽  
Genomic Research ◽  
Laboratory Staff ◽  
Research Perspectives

Introduction: Technological advancements rapidly propel the field of genome research. Advances in genetics and genomics such as the sequence of the human genome, the human haplotype map, open access databases, cheaper genotyping and chemical genomics, have transformed basic and translational biomedical research. Several projects in the field of genomic and personalized medicine have been conducted at the Center for Life Sciences in Nazarbayev University. The prioritized areas of research include: genomics of multifactorial diseases, cancer genomics, bioinformatics, genetics of infectious diseases and population genomics. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine. Results: To further develop genomic and biomedical projects at Center for Life Sciences, the development of bioinformatics research and infrastructure and the establishment of new collaborations in the field are essential.Widespread use of genetic tools will allow the identification of diseases before the onset of clinical symptoms, the individualization of drug treatment, and could induce individual behavioral changes on the basis of calculated disease risk. However, many challenges remain for the successful translation of genomic knowledge and technologies into health advances, such as medicines and diagnostics.It is important to integrate research and education in the fields of genomics, personalized medicine, and bioinformatics, which will be possible with opening of the new Medical Faculty at Nazarbayev University. People in practice and training need to be educated about the key concepts of genomics and engaged so they can effectively apply their knowledge in a matter that will bring the era of genomic medicine to patient care. This requires the development of well-equipped laboratories, bioinformatics, as well as qualified trained physicians and laboratory staff.

Personalized Medicine and Medicinal Chemistry: Toward a Legal Framework in the European Union

2019 ◽  
Vol 18 (25) ◽  
pp. 2165-2173 ◽  
Author(s):  
Aliuska Duardo-Sánchez ◽  
Iñigo De Miguel Beriain
Keyword(s):  
European Union ◽  
Personalized Medicine ◽  
Medicinal Chemistry ◽  
Clinical Care ◽  
Personal Data ◽  
Legal Protection ◽  
Legal Framework ◽  
Sensitive Information ◽  
The European Union ◽  
Massive Sequencing

In the last few years, the fields of Medicinal Chemistry and especially the ones related to the so-called Personalized Medicine, have received a great attention. Significant investment and remarkable researches surround the matter; however, not all those promising advances are reaching patients as quickly as they should. The absence of an adequate regulatory framework could be of no help. The complete and/or massive sequencing of individual genomes faces many ethical-legal challenges. Some of them are access to Personalized Medicine; the treatment of a large volume of sensitive information and the use of tools produced by "big data" systems in clinical care or in predictive models. In addition, the legal protection of personal data related to health, the exercise of autonomy by patients, closely related to the regulation regarding clinical trials, are seriously involved. Our purpose of this work is to review the regulations of the European Union, in an attempt to contribute to a better understanding of the legal framework for the implementation and development of health systems based on Personalized Medicine.

Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity

2022 ◽  
pp. 0272989X2110728
Author(s):  
Anna Heath ◽  
Petros Pechlivanoglou
Keyword(s):  
Personalized Medicine ◽  
Patient Outcomes ◽  
Clinical Care ◽  
Treatment Strategies ◽  
Treatment Decisions ◽  
Personalized Treatment ◽  
Sensitivity Analyses ◽  
Individual Treatment ◽  
Potential Value ◽  
The Individual

Background Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.

Quality of Life for Children with Persistent Sinonasal Symptoms

2003 ◽  
Vol 128 (1) ◽  
pp. 17-26 ◽  
Author(s):  
David J. Kay ◽  
Richard M. Rosenfeld
Keyword(s):  
Quality Of Life ◽  
Clinical Care ◽  
Longitudinal Change ◽  
Good Test ◽  
Routine Clinical Care ◽  
Before And After ◽  
The Mean ◽  
The Individual ◽  
Sinonasal Symptoms

OBJECTIVE: The goal was to validate the SN-5 survey as a measure of longitudinal change in health-related quality of life (HRQoL) for children with persistent sinonasal symptoms. DESIGN AND SETTING: We conducted a before and after study of 85 children aged 2 to 12 years in a metropolitan pediatric otolaryngology practice. Caregivers completed the SN-5 survey at entry and at least 4 weeks later. The survey included 5 symptom-cluster items covering the domains of sinus infection, nasal obstruction, allergy symptoms, emotional distress, and activity limitations. RESULTS: Good test-retest reliability ( R = 0.70) was obtained for the overall SN-5 score and the individual survey items ( R ≥ 0.58). The mean baseline SN-5 score was 3.8 (SD, 1.0) of a maximum of 7.0, with higher scores indicating poorer HRQoL. All SN-5 items had adequate correlation ( R ≥ 0.36) with external constructs. The mean change in SN-5 score after routine clinical care was 0.88 (SD, 1.19) with an effect size of 0.74 indicating good responsiveness to longitudinal change. The change scores correlated appropriately with changes in related external constructs ( R ≥ 0.42). CONCLUSIONS: The SN-5 is a valid, reliable, and responsive measure of HRQoL for children with persistent sinonasal symptoms, suitable for use in outcomes studies and routine clinical care.

scholarly journals Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network

2021 ◽  
Vol 11 (7) ◽  
pp. 647
Author(s):  
Nina R. Sperber ◽  
Olivia M. Dong ◽  
Megan C. Roberts ◽  
Paul Dexter ◽  
Amanda R. Elsey ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Program Implementation ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Implementing Change ◽  
Implementation Outcomes ◽  
Planning Framework

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

scholarly journals Personalized Cancer Medicine in the Media: Sensationalism or Realistic Reporting?

2021 ◽  
Vol 11 (8) ◽  
pp. 741
Author(s):  
Katherine Hicks-Courant ◽  
Jenny Shen ◽  
Angela Stroupe ◽  
Angel Cronin ◽  
Elizabeth F. Bair ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Media Coverage ◽  
Disease Risk ◽  
Federal Drug Administration ◽  
Personalized Cancer Medicine ◽  
The Media ◽  
Cancer Medicine ◽  
Media Reports ◽  
Personalized Cancer ◽  
Standard Tests

Background: Given that media coverage can shape healthcare expectations, it is essential that we understand how the media frames “personalized medicine” (PM) in oncology, and whether information about unproven technologies is widely disseminated. Methods: We conducted a content analysis of 396 news reports related to cancer and PM published between 1 January 1998 and 31 December 2011. Two coders independently coded all the reports using a pre-defined framework. Determination of coverage of “standard” and “non-standard” therapies and tests was made by comparing the media print/broadcast date to the date of Federal Drug Administration approval or incorporation into clinical guidelines. Results: Although the term “personalized medicine” appeared in all reports, it was clearly defined only 27% of the time. Stories more frequently reported PM benefits than challenges (96% vs. 48%, p < 0.001). Commonly reported benefits included improved treatment (89%), prediction of side effects (30%), disease risk prediction (33%), and lower cost (19%). Commonly reported challenges included high cost (28%), potential for discrimination (29%), and concerns over privacy and regulation (21%). Coverage of inherited DNA testing was more common than coverage of tumor testing (79% vs. 25%, p < 0.001). Media reports of standard tests and treatments were common; however, 8% included information about non-standard technologies, such as experimental medications and gene therapy. Conclusion: Confusion about personalized cancer medicine may be exacerbated by media reports that fail to clearly define the term. While most media stories reported on standard tests and treatments, an emphasis on the benefits of PM may lead to unrealistic expectations for cancer genomic care.

Trailblazing Precision Medicine in Europe: a joint view by Genomic Medicine Sweden and the Centers for Personalized Medicine, ZPM, in Germany

2021 ◽  
Author(s):  
Albrecht Stenzinger ◽  
Anders Edsjö ◽  
Carolin Ploeger ◽  
Mikaela Friedman ◽  
Stefan Fröhling ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Precision Medicine ◽  
Genomic Medicine

scholarly journals Indispensability of Clinical Bioinformatics for Effective Implementation of Genomic Medicine in Pathology Laboratories

ACI Open ◽  
2020 ◽  
Vol 04 (02) ◽  
pp. e167-e172
Author(s):  
Srikar Chamala ◽  
Siddardha Majety ◽  
Shesh Nath Mishra ◽  
Kimberly J. Newsom ◽  
Shaileshbhai Revabhai Gothi ◽  
...  
Keyword(s):  
Patient Care ◽  
Clinical Laboratory ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Clinical Pathology ◽  
Genetic Mutation ◽  
Genomic Research ◽  
Pathology Laboratory ◽  
Clinical Bioinformatics ◽  
Effective Implementation

AbstractPatient care is rapidly evolving toward the inclusion of precision genomic medicine when genomic tests are used by clinicians to determine disease predisposition, prognosis, diagnosis, and improve therapeutic decision-making. However, unlike other clinical pathology laboratory tests, the development, deployment, and delivery of genomic tests and results are an intricate process. Genomic technologies are diverse, fast changing, and generate massive data. Implementation of these technologies in a Clinical Laboratory Improvement Amendments-certified and College of American Pathologists-accredited pathology laboratory often require custom clinical grade computational data analysis and management workflows. Additionally, accurate classification and reporting of clinically actionable genetic mutation requires well-curated disease/application-specific knowledgebases and expertise. Moreover, lack of “out of the box” technical features in electronic health record systems necessitates custom solutions for communicating genetic information to clinicians and patients. Genomic data generated as part of clinical care easily adds great value for translational research. In this article, we discuss current and future innovative clinical bioinformatics solutions and workflows developed at our institution for effective implementation of precision genomic medicine across molecular pathology, patient care, and translational genomic research.

scholarly journals Use of Thromboelastography Platelet Mapping for Assessment of Individual Platelet Response Secondary to Oral Antiplatelet Therapy after Percutaneous Coronary Intervention: An Attempt to Start Personalized Antiplatelet Therapy in India

2021 ◽  
Author(s):  
Suvro Sankha Datta ◽  
Dibyendu De ◽  
Nadeem Afroz Muslim
Keyword(s):  
Percutaneous Coronary Intervention ◽  
Antiplatelet Therapy ◽  
Platelet Reactivity ◽  
Coronary Intervention ◽  
Platelet Inhibition ◽  
P Value ◽  
Platelet Response ◽  
Percutaneous Coronary ◽  
The Individual ◽  
Platelet Functions

AbstractHigh on-treatment platelet reactivity (HPR) with P2Y12 receptor antagonists in patients treated with dual antiplatelet therapy (DAPT) is strongly associated with adverse ischemic events after percutaneous coronary intervention (PCI). This prospective study was conducted to assess individual platelet response and HPR to antiplatelet medications in post-PCI cases by thromboelastography platelet mapping (TEG-PM). Total 82 patients who were on aspirin and on either clopidogrel, prasugrel, or ticagrelor were evaluated. The percentage of platelet inhibition to arachidonic acid (AA) and adenosine disdiphosphate (ADP) was calculated by [100-{(MA ADP/AA–MA Fibrin) / (MA Thrombin–MA Fibrin) × 100}], taking 50% response as cut-off for HPR. HPR to clopidogrel and prasugrel was 14.29 and 12.5%, respectively. No HPR was detected to aspirin and ticagrelor. The mean percentage of platelet inhibition was significantly higher in patients with ticagrelor 82.99, 95% confidence interval (CI) of [77.3, 88.7] as compared with clopidogrel 72.21, 95% CI of [65.3, 79.1] and prasugrel 64.2, 95% CI of [52.5, 75.9] (p-value of 0.041 and 0.003, respectively). Aspirin along with ticagrelor is associated with a higher mean percentage of platelet inhibition, and lower HPR as compared with the usage of aspirin combined with clopidogrel or prasugrel. Additionally, it might also be concluded that TEG-PM could be used effectively to measure the individual platelet functions which would make oral antiplatelet therapy more personalized for cardiac patients.

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