The Personalized Medicine Coalition: goals and strategies

Exploring interdisciplinary perspectives on the implementation of personalized medicine and patient-orchestrated care in Alzheimer's disease: A qualitative study within the ABOARD research project

BackgroundThe concepts of 'personalized medicine' and 'patient-orchestrated care' in Alzheimer's disease (AD) lack standard conceptualization, which presents challenges for collaborative and interdisciplinary care.ObjectiveWe explored the interpretations and perspectives of professionals involved in interdisciplinary work on a large-scale project, "ABOARD", with the aim to implement personalized medicine and patient-orchestrated care in AD.MethodsSemi-structured interviews were conducted with 30 professionals and audio-recorded. Two researchers independently coded the data inductively, followed by a thematic analysis.ResultsAccording to professionals across different disciplinary backgrounds (mean age 45.7 years; 53.3% female), personalized medicine pertains to the relevant options that an individual has, informed by biomedical and psychosocial factors, whereas patient-orchestrated care captures factors relevant to the decision-making process. Professionals differed in their views on patient-orchestrated care regarding its desirability and feasibility. The concepts were viewed as similar by professionals, as both involve personal preferences while ultimately assigning responsibility to the clinician. However, implementation challenges persist, and no thematic differences were found between clinicians and other AD-related professionals.ConclusionsAD professionals have shared interpretations and perspectives on implementation of personalized medicine but differed in their views on patient-orchestrated care. Personal preferences are seen as part of personalized medicine, but not yet reflected in definitions in the AD field and beyond. Critical discussions on the challenges and existing doubts are necessary for both personalized medicine and patient-orchestrated care. Multi-level implementation changes are needed for both concepts, which warrants stakeholder involvement as well as support and resources from the entire AD field.

Genomics and multiomics in the age of precision medicine

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Genetically guided precision medicine clinical decision support tools: a systematic review

OBJECTIVES

Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM).

MATERIALS AND METHODS

A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients.

RESULTS

The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy.

DISCUSSION

Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes.

CONCLUSION

We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

3D printing processes in precise drug delivery for personalized medicine

With the advent of personalized medicine, the drug delivery system will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.

Exploring the effectiveness of molecular subtypes, biomarkers, and genetic variations as first-line treatment predictors in Asian breast cancer patients: a systematic review and meta-analysis

BACKGROUND

Breast cancer incidence has been on the rise significantly in the Asian population, occurring at an earlier age and a later stage. The potential predictive value of molecular subtypes, biomarkers, and genetic variations has not been deeply explored in the Asian population. This study evaluated the effect of molecular subtype classification and the presence or absence of biomarkers and genetic variations on pathological complete response (pCR) after neoadjuvant treatment in Asian breast cancer patients.

METHODS

A systematic search was conducted in MEDLINE (PubMed), Science Direct, Scopus, and Cochrane Library databases. Studies were selected if they included Asian breast cancer patients treated with neoadjuvant chemotherapy and contained data for qualitative or quantitative analyses. The quality of the included studies was assessed using the Newcastle Ottawa Scale. Following the random effects model, pooled odds ratios or hazard ratios with 95% confidence intervals for pCR were analysed using Review Manager Software. Heterogeneity between studies was assessed using Cochran's Q-test and I2 test statistics.

RESULTS

In total, 19,708 Asian breast cancer patients were pooled from 101 studies. In the neoadjuvant setting, taxane-anthracycline (TA) chemotherapy showed better pCR outcomes in triple-negative breast cancer (TNBC) (p<0.0001) and human epidermal growth factor receptor 2 enriched (HER2E) (p<0.0001) than luminal breast cancer patients. Similarly, taxane-platinum (TP) chemotherapy also showed better pCR outcomes in TNBC (p<0.0001) and HER2E (p<0.0001). Oestrogen receptor (ER)-negative, progesterone receptor (PR)-negative, HER2-positive and high Ki-67 were significantly associated with better pCR outcomes when treated with either TA or TP. Asian breast cancer patients harbouring wildtype PIK3CA were significantly associated with better pCR outcomes when treated with TA in the neoadjuvant setting (p=0.001).

CONCLUSIONS

In the neoadjuvant setting, molecular subtypes (HER2E and TNBC), biomarkers (ER, PR, HER2, HR, Ki-67, nm23-H1, CK5/6, and Tau), and gene (PIK3CA) are associated with increased pCR rates in Asian breast cancer patients. Hence, they could be further explored for their possible role in first-line treatment response, which can be utilised to treat breast cancer more efficiently in the Asian population. However, it needs to be further validated with additional powered studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021246295.

Biomarker-Guided Tailored Therapy in Major Depression

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Precision medicine in regulatory decision making: Biomarkers used for patient selection in European Public Assessment Reports from 2018 to 2020

Biomarkers can guide precision medicine in clinical trials and practice. They can increase clinical trials' efficiency through selection of study populations more likely to benefit from treatment, thus increasing statistical power and reducing sample size requirements or study duration. We performed a narrative synthesis to explore biomarker utilization for patient selection to guide precision medicine trials in marketing authorization dossiers of centrally approved medicines in Europe between 2018 and 2020 and analyzed in-depth those that eventually included biomarkers in the medicines' indications. From 119 eligible products, 26 included a biomarker in the indication, of which most were oncology products (n = 15). Included biomarkers were often known from literature or from previously approved products in the European Union or the United States. Additionally, 52 dossiers mentioned one or more biomarkers for patient selection in their clinical efficacy and safety information. Although these were not always included in the medicines' indication, they were often implicitly embedded in condition definitions adopted from clinical guidelines or practice. In 15 out of the 26 medicines with a biomarker-guided indication, only biomarker-positive populations were included in the main clinical studies supporting the marketing authorization. These studies were mostly randomized controlled trials or single-arm trials; only two products were studied for multiple indications in an innovative basket trial. Definitions of biomarkers could be subject of debate and needed adaptation after post hoc analyses requested by the assessment committee in four cases, stressing the importance of thorough justification of these definitions to include the right population for an optimal benefit-risk balance, enabling precise medicine.

Institutionalization of personalized medicine in India: analysis of research trends and government interventions

The biggest challenges that any country faces are affordability and accessibility of quality healthcare. Technological advancements can address these challenges. One such advancement is personalized medicine (PM). This paper discusses the implementation and institutionalization of PM. Using the sectoral innovation system framework, this work describes government interventions with research trends in PM in India. The Web of Science database was used to analyze research trends. Indian government-funded interventions to institutionalize PM were compiled and analyzed. Results suggest that India's healthcare sector is dynamic. The framework discusses some specifics, including the research network, boundaries and government initiatives to promote PM adoption. Based on the policy gaps, this paper further proposes an integrated policy framework for incorporating PM into India's healthcare system.

Crosstalk between cancer stem cells and the tumor microenvironment drives progression of premalignant oral epithelium

Cancer stem cells (CSC) are a subpopulation of cancer cells that exhibit properties of self-renewal and differentiation and have been implicated in metastasis and treatment failures. There is mounting evidence that carcinogen-initiated mucosal epithelial stem cells acquire the CSC phenotype following exposure to environmental or infectious mutagens and are responsible for promoting the malignant transformation of premalignant (dysplastic) epithelium. CSC further contribute to the progression of dysplasia by activating signaling pathways through crosstalk with various cell populations in the tumor microenvironment. Two cell types, tumor-associated macrophages (TAM) and vascular endothelial cells (EC) nurture CSC development, support CSC stemness, and contribute to tumor progression. Despite mounting evidence implicating CSC in the initiation and progression of dysplastic oral epithelium to squamous cell carcinoma (SCC), the molecular mechanisms underlying these synergistic biological processes remain unclear. This review will examine the mechanisms that underlie the transformation of normal epithelial stem cells into CSC and the mechanistic link between CSC, TAM, and EC in the growth and the malignant conversation of dysplastic oral epithelium.

Drug Carriers: A Review on the Most Used Mathematical Models for Drug Release

Carriers are protective transporters of drugs to target cells, facilitating therapy under each points of view, such as fast healing, reducing infective phenomena, and curing illnesses while avoiding side effects. Over the last 60 years, several scientists have studied drug carrier properties, trying to adapt them to the release environment. Drug/Carrier interaction phenomena have been deeply studied, and the release kinetics have been modeled according to the occurring phenomena involved in the system. It is not easy to define models’ advantages and disadvantages, since each of them may fit in a specific situation, considering material interactions, diffusion and erosion phenomena, and, no less important, the behavior of receiving medium. This work represents a critical review on main mathematical models concerning their dependency on physical, chemical, empirical, or semi-empirical variables. A quantitative representation of release profiles has been shown for the most representative models. A final critical comment on the applicability of these models has been presented at the end. A mathematical approach to this topic may help students and researchers approach the wide panorama of models that exist in literature and have been optimized over time. This models list could be of practical inspiration for the development of researchers’ own new models or for the application of proper modifications, with the introduction of new variable dependency.

Three-Dimensional Printing for Cancer Applications: Research Landscape and Technologies

As a variety of novel technologies, 3D printing has been considerably applied in the field of health care, including cancer treatment. With its fast prototyping nature, 3D printing could transform basic oncology discoveries to clinical use quickly, speed up and even revolutionise the whole drug discovery and development process. This literature review provides insight into the up-to-date applications of 3D printing on cancer research and treatment, from fundamental research and drug discovery to drug development and clinical applications. These include 3D printing of anticancer pharmaceutics, 3D-bioprinted cancer cell models and customised nonbiological medical devices. Finally, the challenges of 3D printing for cancer applications are elaborated, and the future of 3D-printed medical applications is envisioned.

Digital Twins for Multiple Sclerosis

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

My Data, My Choice? - German Patient Organizations' Attitudes towards Big Data-Driven Approaches in Personalized Medicine. An Empirical-Ethical Study

Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data (‘volume’) from various sources (‘variety’) and varying quality in terms of data accuracy (‘veracity’) to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period (‘velocity’) to provide insights into previously unknown connections and correlations between different items (‘value’). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical concerns have been pointed out by experts in the debate already, moral concerns by stakeholders such as patient organizations (POs) need to be reflected in this context as well. We used an empirical-ethical approach including a website-analysis and 27 telephone-interviews for gaining in-depth insight into German POs’ perspectives on PM and Big Data. Our results show that not all POs are stakeholders in the same way. Comparing the perspectives and political engagement of the minority of POs that is currently actively involved in research around PM and Big Data-driven research led to four stakeholder sub-classifications: ‘mediators’ support research projects through facilitating researcher’s access to the patient community while simultaneously selecting projects they preferably support while ‘cooperators’ tend to contribute more directly to research projects by providing and implemeting patient perspectives. ‘Financers’ provide financial resources. ‘Independents’ keep control over their collected samples and associated patient-related information with a strong interest in making autonomous decisions about its scientific use. A more detailed terminology for the involvement of POs as stakeholders facilitates the adressing of their aims and goals. Based on our results, the ‘independents’ subgroup is a promising candidate for future collaborations in scientific research. Additionally, we identified gaps in PO’s knowledge about PM and Big Data. Based on these findings, approaches can be developed to increase data and statistical literacy. This way, the full potential of stakeholder involvement of POs can be made accessible in discourses around PM and Big Data.

Reliability of algorithmic somatic copy number alteration detection from targeted capture data

Motivation

Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.

Results

On synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%).

Discussion

Whole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods.

Availability and implementation

Scripts are provided as supplementary information.

Supplementary information

Supplementary data are available at Bioinformatics online.

Providers and Patients Caught Between Standardization and Individualization: Individualized Standardization as a Solution Comment on "(Re) Making the Procrustean Bed? Standardization and Customization as Competing Logics in Healthcare"

In their 2017 article, Mannion and Exworthy provide a thoughtful and theory-based analysis of two parallel trends in modern healthcare systems and their competing and conflicting logics: standardization and customization. This commentary further discusses the challenge of treatment decision-making in times of evidence-based medicine (EBM), shared decision-making and personalized medicine. From the perspective of systems theory, we propose the concept of individualized standardization as a solution to the problem. According to this concept, standardization is conceptualized as a guiding framework leaving room for individualization in the patient physician interaction. The theoretical background is the concept of context management according to systems theory. Moreover, the comment suggests multidisciplinary teams as a possible solution for the integration of standardization and individualization, using the example of multidisciplinary tumor conferences and highlighting its limitations. The comment also supports the authors’ statement of the patient as co-producer and introduces the idea that the competing logics of standardization and individualization are a matter of perspective on macro, meso and micro levels.

Persistent effect at 30-month post intervention of a community-based randomized trial of KM2H2 in reducing stroke and heart attack among senior hypertensive patients

BACKGROUND

The effect of the Keep Moving toward Healthy Heart and Healthy Brain (KM2H2) program at 6-month post intervention has been assessed.  The purpose of this study is to evaluate the KM2H² program at 30-month post intervention.

METHODS

A total of 450 senior hypertensive patients from 12 community health centers were randomized by center to either receive KM2H² plus standard care (6 centers, n = 232) or standard care only (6 centers, n = 218). Data for outcome measures at 30-month post intervention were analyzed. New cases of stroke and heart attack were verified with medical records; levels of physical activity were assessed using self-reported questionnaire. In addition to comparative analysis, adjusted incidence rate and program effects were determined using mixed effects modeling method.

RESULTS

At the 30-month follow-up, the adjusted incidence rate [95% CI] of stroke was 11.81% [5.90, 17.72] for patients in the intervention group and 19.78% [14.07, 25.50] (p = 0.03) for the control group. The adjusted incidence rate of heart attack was 3.34% [1.91, 8.58] and 6.68% [1.64, 11.73] for the intervention and control groups (p = 0.16), respectively; the proportion and the duration of engaging in regular physical activity were significantly greater for the intervention group than the control group. The reductions in blood pressure between the intervention and the control was not statistically significant.

CONCLUSIONS

The KM2H² program showed a persistent effect up to 30 months post intervention in enhancing physical activity and reducing the risk of cardio-cerebrovascular events, particularly stroke. These findings demonstrate the persistent effect of the KM2H² and suggest the need for a full-scale evaluation of the intervention program for practical use.

TRIAL REGISTRATION

ISRCTN Register ISRCTN12608966 . Registered 03 March 2015. Retrospectively registered.

Comprehensive benchmarking of SNV callers for highly admixed tumor data

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.

Personalized Medicine in a New Genomic Era: Ethical and Legal Aspects

The genome of two completely unrelated individuals is quite similar apart from minor variations called single nucleotide polymorphisms which contribute to the uniqueness of each and every person. These single nucleotide polymorphisms are of great interest clinically as they are useful in figuring out the susceptibility of certain individuals to particular diseases and for recognizing varied responses to pharmacological interventions. This gives rise to the idea of 'personalized medicine' as an exciting new therapeutic science in this genomic era. Personalized medicine suggests a unique treatment strategy based on an individual's genetic make-up. Its key principles revolve around applied pharmaco-genomics, pharmaco-kinetics and pharmaco-proteomics. Herein, the ethical and legal aspects of personalized medicine in a new genomic era are briefly addressed. The ultimate goal is to comprehensively recognize all relevant forms of genetic variation in each individual and be able to interpret this information in a clinically meaningful manner within the ambit of ethical and legal considerations. The authors of this article firmly believe that personalized medicine has the potential to revolutionize the current landscape of medicine as it makes its way into clinical practice.

Personalized medicine in rheumatology: the paradigm of serum autoantibodies

The sequencing of the human genome is now well recognized as the starting point of personalized medicine. Nonetheless, everyone is unique and can develop different phenotypes of the same disease, despite identical genotypes, as well illustrated by discordant monozygotic twins. To recognize these differences, one of the easiest and most familiar examples of biomarkers capable of identifying and predicting the outcome of patients is represented by serum autoantibodies. In this review, we will describe the concept of personalized medicine and discuss the predictive, prognostic and preventive role of antinuclear antibodies (ANA), anti-citrullinated peptide antibodies (ACPA), rare autoantibodies and anti-drug antibodies (ADA), to evaluate how these can help to identify different disease immune phenotypes and to choose the best option for treating and monitoring rheumatic patients in everyday practice. The importance of ANA resides in the prediction of clinical manifestations in systemic sclerosis and systemic lupus erythematosus and their association with malignancies. ACPA have a predictive role in rheumatoid arthritis, they are associated with the development of a more aggressive disease, extra-articular manifestations and premature mortality in RA patients; moreover, they are capable of predicting therapeutic response. Rare autoantibodies are associated with different disease manifestations and also with a greater incidence of cancer. The determination of ADA levels may be useful in patients where the clinical efficacy of TNF-α inhibitor has dropped, for the assessment of a right management. The resulting scenario supports serum autoantibodies as the cornerstone of personalized medicine in autoimmune diseases.

Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems

Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

Other side of the coin for personalised medicine and healthcare: content analysis of 'personalised' practices in the literature

OBJECTIVES

Various terms and definitions are used to describe personalised approaches to medicine and healthcare, but in ambiguous and inconsistent ways. They mostly have been defined in a top-down manner. However, actual practices might take different paths. Here, we aimed to provide a 'practice-based' perspective on the debate by analysing the content of 'personalised' practices published in the literature.

METHODS

The search in PubMed and EMBASE (April 2014) using the terms frequently used for personalised approaches resulted in 5333 records. 2 independent researchers used different strategies for screening, resulting in 157 articles describing 88 'personalised' practices that were implemented/presented on at least 1 individual/patient case. The content analysis was grounded on these data and did not have a priori analytical frameworks.

RESULTS

'Personalised medicine/healthcare' can be a commodity in the healthcare market, a way how health services are provided, or a keyword for emerging applications. It can help individuals/patients to gain control of their health, health professionals to provide better services, healthcare organisations to increase effectiveness and efficiency, or national health systems to increase performance. Country examples indicated that for integration of practices into health services, attitude towards innovations and health system and policy context is important. Categorisation based on the terms or the technologies used, if any, was not possible.

CONCLUSIONS

This study is the first to provide a comprehensive content analysis of the 'personalised' practices in the literature. Unlike the top-down definitions, our findings highlighted not the technologies but real-life issues faced by the practices. 'Personalised medicine' and 'personalised healthcare' can be differentiated by using the former for specific tools available and the latter for health services with a holistic approach, implemented in certain contexts. To realise integration of 'personalised medicine/healthcare' into real life, science, technology, health policy and practice, and society domains must work together.

A Single-use Strategy to Enable Manufacturing of Affordable Biologics

The current processing paradigm of large manufacturing facilities dedicated to single product production is no longer an effective approach for best manufacturing practices. Increasing competition for new indications and the launch of biosimilars for the monoclonal antibody market have put pressure on manufacturers to produce at lower cost. Single-use technologies and continuous upstream processes have proven to be cost-efficient options to increase biomass production but as of today the adoption has been only minimal for the purification operations, partly due to concerns related to cost and scale-up. This review summarizes how a single-use holistic process and facility strategy can overcome scale limitations and enable cost-efficient manufacturing to support the growing demand for affordable biologics. Technologies enabling high productivity, right-sized, small footprint, continuous, and automated upstream and downstream operations are evaluated in order to propose a concept for the flexible facility of the future.

Big Data and Predictive Analytics: Applications in the Care of Children

Emerging changes in the United States' healthcare delivery model have led to renewed interest in data-driven methods for managing quality of care. Analytics (Data plus Information) plays a key role in predictive risk assessment, clinical decision support, and various patient throughput measures. This article reviews the application of a pediatric risk score, which is integrated into our hospital's electronic medical record, and provides an early warning sign for clinical deterioration. Dashboards that are a part of disease management systems, are a vital tool in peer benchmarking, and can help in reducing unnecessary variations in care.

The genomic CDS sandbox: An assessment among domain experts

Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.

Efficacy of a Community-Based Physical Activity Program KM2H2 for Stroke and Heart Attack Prevention among Senior Hypertensive Patients: A Cluster Randomized Controlled Phase-II Trial

Objective

To evaluate the efficacy of the program Keep Moving toward Healthy Heart and Healthy Brain (KM2H²) in encouraging physical activities for the prevention of heart attack and stroke among hypertensive patients enrolled in the Community-Based Hypertension Control Program (CBHCP).

Design

Cluster randomized controlled trial with three waves of longitudinal assessments at baseline, 3 and 6 months post intervention.

Setting

Community-based and patient-centered self-care for behavioral intervention in urban settings of China.

Participants

A total of 450 participants diagnosed with hypertension from 12 community health centers in Wuhan, China were recruited, and were randomly assigned by center to receive either KM2H² plus standard CBHCP care (6 centers and 232 patients) or the standard care only (6 centers and 218 patients).

Intervention

KM2H² is a behavioral intervention guided by the Transtheoretical Model, the Model of Personalized Medicine and Social Capital Theory. It consists of six intervention sessions and two booster sessions engineered in a progressive manner. The purpose is to motivate and maintain physical activities for the prevention of heart attack and stroke.

Outcome Measures

Heart attack and stroke (clinically diagnosed, primary outcome), blood pressure (measured, secondary outcome), and physical activity (self-report, tertiary outcome) were assessed at the individual level during the baseline, 3- and 6-month post-intervention.

Results

Relative to the standard care, receiving KM2H² was associated with significant reductions in the incidence of heart attack (3.60% vs. 7.03%, p < .05) and stroke (5.11% vs. 9.90%, p<0.05), and moderate reduction in blood pressure (-3.72mmHg in DBP and -2.92 mmHg in DBP) at 6-month post-intervention; and significant increases in physical activity at 3- (d = 0.53, 95% CI: 0.21, 0.85) and 6-month (d = 0.45, 95% CI: 0.04, 0.85) post-intervention, respectively.

Conclusion

The program KM2H² is efficacious to reduce the risk of heart attack and stroke among senior patients who are on anti-hypertensive medication. Findings of this study provide solid data supporting a formal phase-III trial to establish the effectiveness of KM2H² for use in community settings for prevention.

Trial Registration

ISRCTN Register ISRCTN12608966

National Institutes of Health: a catalyst in advancing regenerative medicine science into practice

The stem cell domain of the regenerative medicine field has seen fundamental changes initiated by seminal discoveries in cell biology, genetic engineering, and whole genome sequencing. Many of these discoveries were funded in part by the National Institutes of Health (NIH), and the NIH remains a leader in supporting research in the United States. However, as the field has developed, the NIH has responded proactively to identify roadblocks and to develop solutions that will accelerate translation of basic discoveries to the clinical setting. These activities range from organizing specialized workshops and coordinating activities among international organizations and the different arms of the government to funding small-scale industry. In addition, the NIH has been a key driver in providing needed infrastructure in areas in which the private sector has been unable to, or does not believe it can, invest. These activities of the NIH are as important as its traditional funding role, and I believe they have contributed to the innovation and rapid pace of discovery in this field.

An epidemiological perspective of personalized medicine: the Estonian experience

The Estonian Biobank and several other biobanks established over a decade ago are now starting to yield valuable longitudinal follow-up data for large numbers of individuals. These samples have been used in hundreds of different genome-wide association studies, resulting in the identification of reliable disease-associated variants. The focus of genomic research has started to shift from identifying genetic and nongenetic risk factors associated with common complex diseases to understanding the underlying mechanisms of the diseases and suggesting novel targets for therapy. However, translation of findings from genomic research into medical practice is still lagging, mainly due to insufficient evidence of clinical validity and utility. In this review, we examine the different elements required for the implementation of personalized medicine based on genomic information. First, biobanks and genome centres are required and have been established for the high-throughput genomic screening of large numbers of samples. Secondly, the combination of susceptibility alleles into polygenic risk scores has improved risk prediction of cardiovascular disease, breast cancer and several other diseases. Finally, national health information systems are being developed internationally, to combine data from electronic medical records from different sources, and also to gradually incorporate genomic information. We focus on the experience in Estonia, one of several countries with national goals towards more personalized health care based on genomic information, where the unique combination of elements required to accomplish this goal are already in place.

The need for clinical decision support integrated with the electronic health record for the clinical application of whole genome sequencing information

Whole genome sequencing (WGS) is rapidly approaching widespread clinical application. Technology advancements over the past decade, since the first human genome was decoded, have made it feasible to use WGS for clinical care. Future advancements will likely drive down the price to the point wherein WGS is routinely available for care. However, were this to happen today, most of the genetic information available to guide clinical care would go unused due to the complexity of genetics, limited physician proficiency in genetics, and lack of genetics professionals in the clinical workforce. Furthermore, these limitations are unlikely to change in the future. As such, the use of clinical decision support (CDS) to guide genome-guided clinical decision-making is imperative. In this manuscript, we describe the barriers to widespread clinical application of WGS information, describe how CDS can be an important tool for overcoming these barriers, and provide clinical examples of how genome-enabled CDS can be used in the clinical setting.

A proposed clinical decision support architecture capable of supporting whole genome sequence information

Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

Nursing implications of personalized and precision medicine

OBJECTIVES

Identify and discuss the nursing implications of personalized and precision oncology care.

DATA SOURCES

PubMed, CINAHL.

CONCLUSION

The implications in personalized and precision cancer nursing care include interpretation and clinical use of novel and personalized information including genetic testing; patient advocacy and support throughout testing, anticipation of results and treatment; ongoing chronic monitoring; and support for patient decision-making. Attention must also be given to the family and ethical implications of a personalized approach to care.

IMPLICATIONS FOR NURSING PRACTICE

Nurses face increasing challenges and opportunities in communication, support, and advocacy for patients given the availability of advanced testing, care and treatment in personalized and precision medicine. Nursing education and continuing education, clinical decision support, and health systems changes will be necessary to provide personalized multidisciplinary care to patients, in which nurses play a key role.

The potential application of personalized preventive research

With increases in life expectancy, the focus has shifted to living a healthier, longer life. By concentrating on preventing diseases before occurrence, researchers aim to diminish the increasing gap in medical costs and health inequalities prevalent across many nations. Although we have entered an era of post-genomics, we are still in infancy in terms of personalized preventive research. Personalized preventive research has and will continue to improve with advancements in the use of biomarkers and risk assessment. More evidence based on well-designed epidemiologic studies is required to provide comprehensive preventive medical care based on genetic and non-genetic profile data. The realization of personalized preventive research requires building of evidence through appropriate methodology, verification of results through translational studies as well as development and application of prediction models.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

National Institutes of Health Center for Regenerative Medicine: putting science into practice

The field of regenerative medicine has been revolutionized by breakthroughs in stem cell biology, gene engineering, and whole-genome sequencing. These advances are not only scientific or medical but have also advanced how we conceptualize regenerative medicine. The progenitive research that proceeded as well as a substantial part of the funding that supported these discoveries were provided by the National Institutes of Health (NIH). Now, perhaps more than ever, the NIH has a vital role to play in the translation of science into clinical practice. The NIH is uniquely positioned to coordinate interactions between the different institutes and other arms of the government, as well as international organizations. Efforts of researchers in the United States both within and without the NIH are supported by a number of mechanisms, including specialized workshops, and the support of developing small-scale industry. Additionally, the NIH has stepped up to provide necessary infrastructure in areas of regenerative medicine where the medical need might be apparent but might be currently infeasible or unattractive to private-sector investment. This article will discuss these perhaps lesser-known activities of the NIH, which I believe have continued and will continue to contribute to the role of stem cell research in translating science into regenerative medicine.

Clinic-genomic association mining for colorectal cancer using publicly available datasets

In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.

One size fits one: pharmacogenetics in gastroenterology

Individual variability in response and development of adverse effects to drugs is a major challenge in clinical practice. Pharmacogenomics refers to the aspect of personalized medicine where the patient's genetic information instructs the selection and dosage of therapy while also predicting its adverse effects profile. Sequencing of the entire human genome has given us the opportunity to study commonly used drugs as well as newer therapeutic agents in a new light, opening up opportunities for better drug efficacy and decreased adverse effects. This article highlights developments in pharmacogenomics, relates these to practice of gastroenterology, and outlines roadblocks in translation of this knowledge into clinical practice.

'Personalized medicine': what's in a name?

Over the last decade genomics and other molecular biosciences have enabled new capabilities that, according to many, have the potential to revolutionize medicine and healthcare. These developments have been associated with a range of terminologies, including 'precision', 'personalized', 'individualized' and 'stratified' medicine. In this article, based on a literature review, we examine how the terms have arisen and their various meanings and definitions. We discuss the impact of the new technologies on disease classification, prevention and management. We suggest that although genomics and molecular biosciences will undoubtedly greatly enhance the power of medicine, they will not lead to a conceptually new paradigm of medical care. What is new is the portfolio of modern tools that medicine and healthcare can use for better targeted approaches to health and disease management, and the sociopolitical contexts within which these tools are applied.

Metabolomic profiling in colorectal cancer: opportunities for personalized medicine

Colorectal cancer (CRC) is one of the most common types of cancer in the world, with high prevalence and mortality. Understanding the alterations of cancer metabolism and identifying reliable biomarkers would facilitate the development of novel technologies of CRC screening and early diagnosis, as well as new approaches to providing personalized medicine. Metabolomics, as an emerging molecular phenotyping approach, provides a clinical platform technology with an unprecedented amount of metabolic readout information, which is ideal for theranostic biomarker discovery. Metabolic signatures can link the unique pathophysiological states of patients to personalized health monitoring and intervention strategies. This article presents an overview of the metabolomic studies of CRC with a focus on recent advances in the biomarker discovery in serum, urine, fecal water and tissue samples for cancer diagnosis. The development and application of metabolomics towards personalized medicine, including early diagnosis, cancer staging, treatment and drug discovery are also discussed.

Tissue banking, bioinformatics, and electronic medical records: the front-end requirements for personalized medicine

Personalized medicine promises patient-tailored treatments that enhance patient care and decrease overall treatment costs by focusing on genetics and "-omics" data obtained from patient biospecimens and records to guide therapy choices that generate good clinical outcomes. The approach relies on diagnostic and prognostic use of novel biomarkers discovered through combinations of tissue banking, bioinformatics, and electronic medical records (EMRs). The analytical power of bioinformatic platforms combined with patient clinical data from EMRs can reveal potential biomarkers and clinical phenotypes that allow researchers to develop experimental strategies using selected patient biospecimens stored in tissue banks. For cancer, high-quality biospecimens collected at diagnosis, first relapse, and various treatment stages provide crucial resources for study designs. To enlarge biospecimen collections, patient education regarding the value of specimen donation is vital. One approach for increasing consent is to offer publically available illustrations and game-like engagements demonstrating how wider sample availability facilitates development of novel therapies. The critical value of tissue bank samples, bioinformatics, and EMR in the early stages of the biomarker discovery process for personalized medicine is often overlooked. The data obtained also require cross-disciplinary collaborations to translate experimental results into clinical practice and diagnostic and prognostic use in personalized medicine.

Clinical practice on the horizon: personalized medicine

With the advent of the human genome project, we have never known so much about the uniqueness of individuals. Personalized medicine is poised to use this genetic and genomic information along with the impact of environment and clinical presentation to provide healthcare from an individual perspective. This offers the opportunity to improve our ability to diagnose and predict disease, provide earlier intervention, identify new treatment regimens, and address the safety and efficacy of drug use. The impact of personalized medicine to our current model of healthcare delivery is tremendous, and although strides have been made, there are still challenges and barriers to overcome before personalized medicine can be fully implemented. Advanced practice nurses may not be fully aware of the personalized medicine initiative or may not be well versed on genetic and genomic content, which is a key concept of personalized medicine. The role of advanced practice nurses is an integral part of the healthcare system, and as such, they are poised to be key providers and contributors to personalized medicine. The personalized medicine initiative is discussed along with examples of genetic and genomic information that lend to our understanding, diagnosis, and treatment of disease, as well as the role and responsibilities of advanced practice nurses. Resources for personalized medicine and genetic and genomic content are provided.

Clinical decision support for genetically guided personalized medicine: a systematic review

OBJECTIVE

To review the literature on clinical decision support (CDS) for genetically guided personalized medicine (GPM).

MATERIALS AND METHODS

MEDLINE and Embase were searched from 1990 to 2011. The manuscripts included were summarized, and notable themes and trends were identified.

RESULTS

Following a screening of 3416 articles, 38 primary research articles were identified. Focal areas of research included family history-driven CDS, cancer management, and pharmacogenomics. Nine randomized controlled trials of CDS interventions for GPM were identified, seven of which reported positive results. The majority of manuscripts were published on or after 2007, with increased recent focus on genotype-driven CDS and the integration of CDS within primary clinical information systems.

DISCUSSION

Substantial research has been conducted to date on the use of CDS to enable GPM. In a previous analysis of CDS intervention trials, the automatic provision of CDS as a part of routine clinical workflow had been identified as being critical for CDS effectiveness. There was some indication that CDS for GPM could potentially be effective without the CDS being provided automatically, but we did not find conclusive evidence to support this hypothesis.

CONCLUSION

To maximize the clinical benefits arising from ongoing discoveries in genetics and genomics, additional research and development is recommended for identifying how best to leverage CDS to bridge the gap between the promise and realization of GPM.