Precision Medicine

Precision Medicine is still an evolving research and policy space some 18 months after the initial White House policy announcement.
• There is little evidence of a mainstream public discourse about the PMI yet.
• These audiences get their information directly from the media statements, rather than journalists
• Concerns about the use of genetic screening to target and segment populations for treatment have been discussed by academics such as Charmaine Royal, Jonathan Kahn and Dorothy Roberts. The PMI brings such concerns to the attention of policy-makers.
• There is little evidence of Australian involvement in online discussions about the PMI.

It is expected that in 2020 more than 17,000 cases of colorectal cancer will be diagnosed in The Netherlands. To date, patients are included in a surgeon-led follow-up programme whose main focus is recurrence detection. However, patients often experience multiple physical and psychosocial problems. Currently, these problems are not always encountered. More care by a generalist is suggested as a solution. Furthermore, patients prefer to undergo rehabilitation in their own environment and to be more involved in their own health care. eHealth applications might enhance this. Oncokompas(2.0) is an online self-management application which facilitates access to supportive care. This study aims to evaluate primary care follow-up and aftercare in comparison with secondary care follow-up and aftercare for patients with colon cancer. Second, the added value of Oncokompas(2.0) to care will be assessed. This is a multi-centre 2 × 2 factorial randomised controlled trial with a calculated sample ...

Drug hypersensitivity reactions to chemotherapy agents are an important problem that Allergy Departments should be proactively prepared to confront, by meeting the necessary high standards to offer optimal management pathways, including high-risk techniques such as drug provocation testing or rapid drug desensitisation, which will cost-effectively improve the prognosis of many patients. Comprehensive risk management strategies, access to expert staff, specific diagnostic techniques, and a dedicated allergy-led area are fundamental to ensure safety. In this manuscript, we will explore current diagnostic and management pathways for hypersensitivity reactions to chemotherapy agents.

Personalized medicine has the potential to allow patients to receive drugs specific to their individual disease, and to increase the efficiency of the healthcare system. There is currently no comprehensive overview of personalized medicine, and this research aims to provide an overview of the concept and definition of personalized medicine in nine European countries. A targeted literature review of selected health databases and grey literature was conducted to collate information regarding the definition, process, use, funding, impact and challenges associated with personalized medicine. In-depth qualitative interviews were carried out with experts with health technology assessment, clinical provisioning, payer, academic, economic and industry experience, and with patient organizations. We identified a wide range of definitions of personalized medicine, with most studies referring to the use of diagnostics and individual biological information such as genetics and biomarkers. Few st...

PRECISION & PERSONALIZED  MEDICINE IN ADVANCED KIDNEY DISEASES MEDIATED BY TARGETED MOLECULAR TREATMENTS BASED ON  PHARMACOMULTIOMICS, EPIDRUGS & NANOMEDICINE.
Prof Dr John Giannios, Leader of the Postgraduate Programme  in GENOMIC-MEDICINE & HEALTHCARE, UNIVERSITY OF SOUTHWALES, GREAT BRITAIN, and Dr Alice Bleta, Dept of Nephrology, M.Hospital, Athens, Greece.
Advanced kidney diseases including chronic kidney-disease (CKD), renal-fibrosis, diabetic-nephropathy, glomerulonephritis, polycystic kidney-disease, nephrotic-syndrome, kidney-failure, end-stage renal-disease, autoimmune disease-related kindey-disease (ADRKD) involving rheumatoid-arthritis or systemic lupus-erythematosus (SLE), rare kidney-diseases consisting of Wegener-granulomatosis, goodpature-syndrome or Alport-syndrome, and even kidney-malignancy, such as hypernephroma or renal-adenocarcinoma or renal-cell carcinoma (RCC), and other types of kidney-disease are responsible for a great percentage of  morbidity and mortality, globally. This percentage of morbidity and mortality is enhanced by close inter-relation with cardiovascular-diseases (CVDs) with approximately ten percent of all the cardiovascular-deaths to be caused by the decreased-rate of glomerular-filtration which is a clinical-phenotypic sign of chronic kidney-disease (CDK). Other noncommunicable-diseases associated to kidney-failure or ESRD consist of adult-onset diabetes or type II diabetes and hypertension, while communicable-diseases linked to kidney-diseases consist of covid-19, etc. 
Thus, kidney diseases require radical improvements involving their screening, diagnosis, prevention, monitoring, prognosis and mainly treatment, which has to be tailored to the clinical phenotype of each individual case of kidney disease that has to be linked to the disease related genotype, and mainly epigenotype for the implementation of precision and personalized medicine approaches.
Epigenomic alterations which may be detected by pharmacoepigenomics may activate the pathogenesis of kidney-diseases  by interfering with the expression of disease related protein coding genes. This phenomenon may also affect the clinical response to conventional therapeutic approaches by creating drug resistance. Also, epigenomic testing may circumvent the diagnostic odyssey of kidney diseases  and since epigenomic alterations are reversible, we can prevent the development of kidney-diseases by influencing the associated expression of the disease related deleterious variants in protein coding genes. In case the kidney-disease has already been developed, we can tailor precision and personalized medicine approaches by targeting the disease related deleterious epigenomic alterations, and variants in protein coding genes by combining results from pharmacogenomics and pharmacoepigenomics for eradicating the disease, suppress its symptoms or improve the sensitivity and therapeutic index of the administered therapeutic agents. In this process, we may also utilize personalized data generated by kidney-disease multiomics including nutrigenomics, metabolomics, lipidomics, immunogenomics, interactomics, glycomics, transcriptomics, proteomics, epiptoteomics, epitranscriptomics, genomics, DNA and RNA epigenomics, etc. Furthermore, Next Generation Sequencing or NGS may lead to the discovery of novel kidney disease related genes and variants, which may be used as novel biomarkers for screening, diagnosis, prevention, monitoring, prognosis and mainly for targeted therapy.
Emphasis must be given on the reversible epigenetic alterations which may interact with each other creating the new discipline of epi-interactomics, which includes interactions among post-translational alterations of histone-tails including phosphorylation, methylation, ubiquitination, deacetylation and mainly acetylation, post-transcriptional alteration by RNA-EDITING, post-transcriptional regulation of gene-expression and chromatin-remodelling by noncoding or non-messenger RNA- genes which may be regulated genomically and epigenomically , and consist of sncRNAs such as microRNAs for inhibiting targeted mRNA, nephro-miRNAs, nephro-isomirs, nephropharmaco-miRNAs, epi-miRNAs, nephrotargo-miRNAs and siRNAs, lncRNA,  PIWI-RNA, DNA-methylation including CpG-islands, shores, shells, promoter-methylation, SINE & LINE-methylation, chromatin-readers, chromatin-folding proteins, such as SATB-1, etc.   
Epidrugs which may prove to be very efficient against kidney-diseases  include chromatin-modifying epidrugs including DNA-methylation(DNMT1) inhibitors and histone-deacetylase(HDAC) inhibitors which may reactivate transcriptionally the expression of noncoding-RNA genes and protein-coding genes, histone-demethylase (HDT) inhibitors, histone-acetyltransferase (HAT) inhibitors, histone-methyltransferase (HMT) inhibitors, regulators of Polycomb protein-group, epitranscriptomic-regulation of RNA-modifications mediated by epigenomic-readers, such as BRD-4, epigenomic-erasers, such as LSD-1,and  epigenomic-writers including EZH-2 and DOT-1L,epigenomic insulators, shapers or movers,  inhibitors of chromatin-readers including bromodomains and Extra-Terminal or BET, SIRT and JMJ-inhibitors, chromosome-remodeling agents targeting nucleosome-sliding and nucleosome-reconstitution, forward and reverse epipharmacogenomics, miRNA-mimics, miRNA-antagonists such as antagomirs, antisense RNA-oligonucleotides, locked nucleic-acids, PNA-antagomirs,  antimiRNA-oligos, miRNA-sponges, miRNA-agomirs, block-miRNAs, replacement-therapies, aptamers, epigenomic-editing, A to I RNA-editing via ADAR, RNAi, modulators of gene-regulatory networks, CRISP GENOME-EDITING, regenerative approaches mediated by induced-pluripotent Stem-Cells, etc.   
Clinical-molecular diagnostic-methods which may be used consist of Bisulfite-sequencing, DNA-methylation analysis, Oxidative-Bisulfite Seq, MRE-sequencing, MBD-sequencing, WGBS-methyl sequencing, OXBS sequencing, ChIP-exo, Methyl CpG-Binding domain-sequencing, MeDip-DNA immunoprecipitation, MS/HMR-analysis, Methylation specific PCR,  or MSP, DNA-methylation microarray-analysis, bisulfite-sequencing PCR-analysis, LC/MS-MS, Chip-sequencing, epitranscriptomic-sequencing with MeRIP-sequencing and m6A-sequencing, third next-generation REAL TIME sequencing, MNase sequencing, DNase sequencing, FAIRE sequencing, smRNA sequencing, mRNA sequencing, etc.     
The future potential of multiomic based treatments which can be delivered to cellular and molecular targets by nanomedicine-systems shall be taken from lab to clinic by the implementation of Translational Medicine for tailoring personalized, and precision medicine approaches.  Nanodelivery systems may transport therapeutic molecular agents to the targeted tissues, cells, and molecules by circumventing biological milieu interactions and avoiding RES elimination enhancing therapeutic index, while reducing and eliminating systemic toxicity. This way, we can circumvent in the future the traditional-medicine approach of one size fits all, and proceed to the implementation of stratified or precision-medicine, and personalized-medicine for each kidney-disease patient based on multiomic-evidence improving screening, diagnosis, prevention, prognosis, monitoring and mainly treatment of currently incurable kidney-diseases.

Little is known about the efficacy of personalized nutrition (PN) interventions for improving consumption of a Mediterranean diet (MedDiet). The objective was to evaluate the effect of a PN intervention on dietary changes associated with the MedDiet. Participants (n = 1607) were recruited into a 6-mo, Internet-based, PN randomized controlled trial (Food4Me) designed to evaluate the effect of PN on dietary change. Participants were randomly assigned to receive conventional dietary advice [control; level 0 (L0)] or PN advice on the basis of current diet [level 1 (L1)], diet and phenotype [level 2 (L2)], or diet, phenotype, and genotype [level 3 (L3)]. Dietary intakes from food-frequency questionnaires at baseline and at 6 mo were converted to a MedDiet score. Linear regression compared participant characteristics between high (>5) and low (≤5) MedDiet scores. Differences in MedDiet scores between treatment arms at month 6 were evaluated by using contrast analyses. At baseline, high...

Imaging Mass Cytometry (IMC) is an expansion of mass cytometry, but rather than analyzing single cells in suspension, it uses laser ablation to generate plumes of particles that are carried to the mass cytometer by a stream of inert gas. Images reconstructed from tissue sections scanned by IMC have a resolution comparable to light microscopy, with the high content of mass cytometry enabled through the use of isotopically labeled probes and ICP-MS detection. Importantly, IMC can be performed on paraffin-embedded tissue sections, so can be applied to the retrospective analysis of patient cohorts whose outcome is known, and eventually to personalized medicine. Since the original description in 2014, IMC has evolved rapidly into a commercial instrument of unprecedented power for the analysis of histological sections. In this Review, we discuss the underlying principles of this new technology, and outline emerging applications of IMC in the analysis of normal and pathological tissues. © ...

The American Joint Committee on Cancer (AJCC) staging manual has become the benchmark for classifying patients with cancer, defining prognosis, and determining the best treatment approaches. Many view the primary role of the tumor, lymph node, metastasis (TNM) system as that of a standardized classification system for evaluating cancer at a population level in terms of the extent of disease, both at initial presentation and after surgical treatment, and the overall impact of improvements in cancer treatment. The rapid evolution of knowledge in cancer biology and the discovery and validation of biologic factors that predict cancer outcome and response to treatment with better accuracy have led some cancer experts to question the utility of a TNM-based approach in clinical care at an individualized patient level. In the Eighth Edition of the AJCC Cancer Staging Manual, the goal of including relevant, nonanatomic (including molecular) factors has been foremost, although changes are mad...

Clinical predictions using clinical data by computational methods are common in bioinformatics. However, clinical predictions using information from genomics datasets as well is not a frequently observed phenomenon in research. Precision medicine research requires information from all available datasets to provide intelligent clinical solutions. In this paper, we have attempted to create a prediction model which uses information from both clinical and genomics datasets. We have demonstrated multiclass disease predictions based on combined clinical and genomics datasets using machine learning methods. We have created an integrated dataset, using a clinical (ClinVar) and a genomics (gene expression) dataset, and trained it using instance-based learner to predict clinical diseases. We have used an innovative but simple way for multiclass classification, where the number of output classes is as high as 75. We have used Principal Component Analysis for feature selection. The classifier predicted diseases with 73% accuracy on the integrated dataset. The results were consistent and competent when compared with other classification models. The results show that genomics information can be reliably included in datasets for clinical predictions and it can prove to be valuable in clinical diagnostics and precision medicine.

Cancer is the second leading cause of death globally. One of the main hallmarks in cancer is the functional deregulation of crucial molecular pathways via driver genetic events that lead to abnormal gene expression, giving cells a selective growth advantage. Driver events are defined as mutations, fusions and copy number alterations that are causally implicated in oncogenesis. Molecular analysis on tissues that have originated from a wide range of anatomical areas has shown that mutations in different members of several pathways are implicated in different cancer types. In recent decades, significant efforts have been made to incorporate this knowledge into daily medical practice, providing substantial insight towards clinical diagnosis and personalized therapies. However, since there is still a strong need for more effective drug development, a deep understanding of the involved signaling mechanisms and the interconnections between these pathways is highly anticipated. Here, we perform a systemic analysis on cancer patients included in the Pan-Cancer Atlas project, with the aim to select the ten most highly mutated signaling pathways (p53, RTK-RAS, lipids metabolism, PI-3-Kinase/Akt, ubiquitination, b-catenin/Wnt, Notch, cell cycle, homology directed repair (HDR) and splicing) and to provide a detailed description of each pathway, along with the corresponding therapeutic applications currently being developed or applied. The ultimate scope is to review the current knowledge on highly mutated pathways and to address the attractive perspectives arising from ongoing experimental studies for the clinical implementation of personalized medicine.

It is widely accepted that cancer develops as a results of mutations in cell growth pathway related genes particularly those mutations that occur in growth factor receptor pathways such as the cell surface Epidermal Growth Factor Receptor (EGFR) and Fibroblast Growth Factor Receptor (FGFR) pathways and in tumor suppressor genes such as p53, APC and PTEN. Detection of these mutations is critical not only for early detection of cancerous cells but also for decisions on what therapy should be applied to treat the patient.

Medicine has been personal long before the concept of “personalized medicine” became popular. Health professionals have always taken into consideration the individual characteristics of their patients when diagnosing, and treating them. Patients have cared for themselves and for each other, contributed to medical research, and advocated for new treatments. Given this history, why has the notion of personalized medicine gained so much traction at the beginning of the new millennium?

Personalized Medicine investigates the recent movement for patients’ involvement in how they are treated, diagnosed, and medicated; a movement that accompanies the increasingly popular idea that people should be proactive, well-informed participants in their own healthcare.

While it is often the case that participatory practices in medicine are celebrated as instances of patient empowerment or, alternatively, are dismissed as cases of patient exploitation, Barbara Prainsack challenges these views to illustrate how personalized medicine can give rise to a technology-focused individualism, yet also present new opportunities to strengthen solidarity. Facing the future, this book reveals how medicine informed by digital, quantified, and computable information is already changing the personalization movement, providing a contemporary twist on how medical symptoms or ailments are shared and discussed in society.

Bringing together empirical work and critical scholarship from medicine, public health, data governance, bioethics, and digital sociology, Personalized Medicine analyzes the challenges of personalization driven by patient work and data. This compelling volume proposes an understanding that uses novel technological practices to foreground the needs and interests of patients, instead of being ruled by them.

This essay examines racial thinking in the emergence and unfolding of pharmacogenetic science in the United States.  It also looks at government funding structures for what is now called "precision medicine" to show how race thinking structures scientific sorting practices as concerns human DNA for medical genetics.  It ends with a discussion of then Senator Obama's Personal Medicine Act of 2006 to offer some thoughts on how to move forward with conceptualizing human genetic diversity without falling into the facility of race thinking in genomic science.

The genetics revolution that will transform our healthcare, the way we make babies, the nature of the babies we make, and ultimately our evolution as a species has already begun. It will arrive far sooner than most of us think and will transform our lives and the world around us in ways we can hardly imagine - and we aren't ready. If you have a sense that the genetics revolution is important but recognize you need to know more about what’s happening, what’s at stake, what this means for you, and what you should be doing now to prepare.

Could this be the most important book of our generation?

The American Joint Committee on Cancer (AJCC) staging manual has become the benchmark for classifying patients with cancer, defining prognosis, and determining the best treatment approaches. Many view the primary role of the tumor, lymph node, metastasis (TNM) system as that of a standardized classification system for evaluating cancer at a population level in terms of the extent of disease, both at initial presentation and after surgical treatment, and the overall impact of improvements in cancer treatment. The rapid evolution of knowledge in cancer biology and the discovery and validation of biologic factors that predict cancer outcome and response to treatment with better accuracy have led some cancer experts to question the utility of a TNM-based approach in clinical care at an individualized patient level. In the Eighth Edition of the AJCC Cancer Staging Manual, the goal of including relevant, nonanatomic (including molecular) factors has been foremost, although changes are mad...

The biopsychosocial model is among the most influential frameworks for human-centered health improvement but has faced significant criticism-both conceptual and pragmatic. This paper extends and fundamentally restructures the biopsychosocial model by combining it with sociotechnical systems theory. The resulting biopsychosociotechnical model addresses key critiques of the biopsychosocial model, providing a more "practical theory" for humancentered health improvement. It depicts the determinants of health as complex adaptive system of systems; includes the the artificial world (technology); and provides a roadmap for systems improvement by: differentiating between "health status" and "health and needs assessment", [promoting problem framing]; explaining health as an emergent property of the biopsychosociotechnical context [imposing a systems orientation]; focusing on "interventions" vs. "treatments" to modify the biopsychosociotechnical determinants of health, [expanding the solution space]; calling for a participatory design process [supporting systems awareness and goal-orientation]; and including intervention management to support the full lifecycle of health improvement.

This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. The sensing te...

What happens to patients with cancer engaged in biomedical research when intellectual property regimes and ethical regimes intersect? This qualitative historical study addresses this question by situating the experiences, hopes, and reasons of patients to enter clinical trials within the historical trajectory of informed consent and monoclonal antibodies, the biotechnology underpinning many targeted drugs used in oncological clinical trials and biobank research. Based on fieldwork we undertook in a German university hospital where we interviewed patients and the medical personnel, a historical review, and an ethical analysis we inquire into the effects that financial, legal, and technological changes connected to the relevant pharmaceutical research and commerce have on cancer patients engaged in clinical trials and biobank research. We find that the controversial aspects of monoclonal antibodies, especially those related to the commercial interests at stake, enter the informed consent process mainly in the form of informative gaps. We highlight how a qualitative analysis of the clinic, especially when it is situated against the backdrop of the history of related technological advancements and patent regime, it can serve the purpose of giving voice to subjects who are silenced by regimes of an ethical, epistemic, and commercial kind while pointing to informed consent as an unhelpful device for addressing risks arising from the commercial purposes of biomedical products and infrastructure.

The twenty-first century paradigm of precision medicine can be characterized by its joint commitments to (1) the revision of traditional nosological systems, (2) the utilization of transformative new methods of data collection and analysis ("big data"), and (3) the employment of scientific methods able to reduce complex phenomenological, behavioral, and physiological signs and symptoms to underlying biomechanisms. This chapter assesses the value of these commitments for psychiatry, and concludes that they are collectively neither necessary nor sufficient for progress in the explanation of, and intervention upon, mental disorders. Each hold promise, and has proved transformative in some areas of psychiatric research and practice, but their appropriateness is better assessed independently and circumstantially. Accordingly, the value of other traditional psychiatric commitments-such as to the principled demarcation the pathological from the normal, and the prioritization of research that has clinical application-should not be abandoned amidst the current vogue for precision.

For over a decade the term “Big data” has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, “Big data” no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as “data analytics” and “data science” have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises “Big Advances,” significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set “Big data” analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.

The progress of so-called "personalized medicine", in particular in the context of the development of targeted therapies in oncology, leads to more precise definitions, diagnostics and treatments of diseases. This could give rise to the constitution of ever smaller classes of diseases. Further, via an approach that aims to be both dynamic and holistic, "systems medicine" and in particular "P4 medicine" (preventive, predictive, participative, and personalized) advocates an individualization based on observation of the transition between health and disease for each individual, thanks to repeated monitoring of various genomic, clinical, psychical, environmental, and social characteristics. These different forms of individualization echo the theses of Canguihem, according to which there is no general definition of the normal and the healthy but only a universal biological normativity, which, in each biological individual, takes an individual form. That idea led Canguilhem to affirm the impossibility both of a science of the normal and the pathological and of complete objectivity with regard to these phenomena. Systems medicine, by contrast, and in particular P4 medicine, promotes both a quantification and a science of well-being. The aim of this chapter is to examine the forms of individualization of health and disease which give rise to the two principal orientations of personalized medicine-the stratified medicine, the precision medicine, already used in oncology, and the more theoretical systems medicine-and then to confront them with Canguilhem's thesis regarding health and disease. In this way, I will develop a critical analysis of the promises and pretentions of personalized medicine, and in particular P4 medicine.

Until recently, a one-drug-fits-all model was applied to every patient diagnosed with the same condition. But not every condition is the same, and this has led to many cases of ineffective treatment. Pharmacogenetics is increasingly used to stratify patients for precision medicine treatments, for instance, the UGT1A1*28 polymor-phism as a dosage indicator for the use of irinotecan as well as epidermal growth factor receptor (EGFR) immunohistochemistry and KRAS Proto-Oncogene (KRAS) exon 2 mutation tests for determining the likelihood of treatment response to cetuximab or panitumumab treatment in metastatic colorectal cancer (CRC). The other molecular subtypes, such as KRAS exon 3/4, B-Raf Proto-Oncogene, NRAF, PIK3CA, and PETN, were also reported as potential new pharmacogenetic targets for the current and the newly discovered anticancer drugs. In addition to next-generation sequencing (NGS), primary tumor cells for in vivo and in vitro drug screening, imaging biomarker 3 0-Deoxy-3 0-18F-fluorothymidine positron emission tomography, and circulating tumor DNA (ctDNA) detection methods are being developed and may represent the future direction of precision medicine. This review will discuss the current environment of precision medicine, including clinically approved targeted therapies, the latest potential therapeutic agents, and the ongoing pharmacogenetic trials for CRC patients.

An alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has so far underappreciated the third alphabet of life, the “sugar code” (i.e., the information stored in glycans, glycoproteins, and glycolipids).

This article examines the ways in which contemporary debates and values around the third alphabet of life are shaped by history and politics of the sugar code, and the socio-materiality of the cell.

Similar to all fields of study, growth occurs with breakthrough or developmental research spanning to the progress and intimate utility of technology. Dilemmas of various sectors have been favorably resolved by adopting artificial intelligence (AI) algorithms. Applying Precision Medicine profoundly depends on AI algorithms to work Precision Medicine queries like; predicting or detecting, diagnosing the disease properly, and optimizing therapy, hence, the selection of the algorithm is affected by its capacity and practicality. Nevertheless, it is yet in its initial step and fronts some hurdles crucial to the flourishing deployment of precision medicine like research, adoption values, and authority controls. Notwithstanding, Precision Medicine also pretends some difficulties like; modifying the health discipline and profession to the fact that automata and algorithms could displace most of the healthcare professional tasks they act now. Ultimately, effective employment of precision medicine would rescue countless lives and improve the health profession. This review examines the present state of AI applications in precision medicine and future opportunities. It discusses major AI systems like IBM Watson, examining moral accountability and legal obligations when applying it in clinical decision-making, advantages and boundaries of employing Watson and different AI clinical decision support techniques, and considerations before consulting AI systems.

L’Evidence-Based Medicine (EBM) et la médecine personnalisée (MP) ont une ambition commune : réduire l’écart entre les résultats de la recherche biomédicale et leur application en clinique. Toutefois la MP est souvent présentée comme un « nouveau paradigme » pour la médecine, comme l’avait d’ailleurs été l’EBM dans les années 1990. Elle recouvre beaucoup de projets différents mais au cœur de la plupart d’entre eux, on retrouve la prétention d’une meilleure prise en compte des spécificités individuelles que ne le permettrait l’EBM avec son approche populationnelle et statistique. Dans cet article, nous concentrons notre attention sur la MP en cancérologie : elle concerne essentiellement un ciblage des traitements à partir de caractéristiques moléculaires des patients. Ce ciblage est rendu notamment possible par la meilleure connaissance des mécanismes moléculaires des cancers. Il conduit à une stratification en sous-groupes plus restreints de patients et met en difficulté l’évaluation classique des thérapeutiques promue par l’EBM, en particulier, les essais cliniques randomisés qui reposent sur des cohortes de grande taille. Mais la meilleure connaissance des mécanismes et la plus grande précision des thérapies pourraient rendre ces essais moins nécessaires. Assistons-nous avec la MP à une revanche de la culture physiopathologique et mécaniste sur celle statistique et empiriste au sein de la recherche clinique ? Notre objectif est de mettre en évidence ce qui, pour cette MP déjà effective, conduit à des changements épistémologiques dans la manière de considérer ce qui compte comme type d’information et de preuve en médecine, en particulier dans le champ de l’évaluation thérapeutique. Nous défendons l’idée que la MP, loin de pouvoir se passer des approches statistiques et de la mise en évidence de corrélation, place en réalité l’EBM face à de nouveaux défis. Elle la conduit à renforcer l’articulation et l’intégration des données statistiques et mécanistes pour une meilleure prise en charge de chaque patient.

Evidence-Based Medicine (EBM) and Personalized Medicine (PM) share a common goal: reducing the gap between the results of biomedical research and their clinical application. PM is, however, often presented as a “new paradigm” for medicine, just as EBM was in the 1990s. It covers a wide variety of projects but the core idea that generally unites them is the ambition of better taking account of individual specificities than did EBM with its statistical and population-centred approach. In this article, I concentrate on PM in cancerology, the essence of which is to target treatments based on the molecular profile of the patient. This targeting is made possible by gaining better knowledge about the molecular mechanisms of cancers. The classification of patients as a function of their molecular profile entails the creation of patient sub-groups. This creates a problem for the traditional evaluation of therapeutic treatment promoted by EBM, in particular the use of randomized trials using sizeable cohorts. But a better understanding of the mechanisms and the greater precision of treatments could reduce the need for these trials. Does PM thus represent the revenge of a physio-pathological and mechanistic culture in clinical research against the statistical and empirical one of EBM? My objective is to show how current practices of PM leads to epistemological changes in our estimation of what count as relevant types of information and proof in medicine, in particular in the field of therapeutic evaluation. I defend the idea that PM, far from obviating the need for statistical approaches and the search for correlations, ultimately poses new challenges for EBM. PM drives EBM to strengthen the articulation and the integration of statistical and mechanistic data with a view to providing a better service for each patient.

Current visions of precision medicine are contributing to an increasing polarization within healthcare: boutique-style personalized medicine could be available for the select few who can afford to pay for the interpretation of their health data by humans, while the rest of the population will be expected to resort to automated medicine for most of their health concerns. This scenario should be avoided. Expanding the goals of precision medicine to include 'subjective' personal outcomes as well as social outcomes that are measured and (also financially) rewarded alongside clinical outcomes would be a step in this process. At the same time, the realization of just access to healthcare, and the improvement of social determinants, is a necessary precondition for precision medicine to benefit societies.

Epigenetic alterations are considered to be very influential in both the normal and disease states of an organism. These alterations include methylation, acetylation, phosphorylation, and ubiquitylation of DNA and histone proteins (nucleosomes) as well as chromatin remodeling. Many diseases, such as cancers and neurodegenerative disorders, are often associated with epigenetic alterations. DNA methylation is one important modification that leads to disease. Standard therapies are given to patients; however, few patients respond to these drugs, because of various molecular alterations in their cells, which may be partially due to genetic heterogeneity and epigenetic alterations. To realize the promise of personalized medicine, both genetic and epigenetic diagnostic testing will be required. This review will discuss the advances that have been made as well as the challenges for the future.

Despite its vagueness, ‘Personalized’, ‘Precision’, ‘P4’, ‘P5’, ‘individualized’, ‘stratified medicine’—or p-medicine in short—has become an increasingly popular term in biomedical literature. Philosophers have attempted to analyze what these various terms involve and have discussed consequences for medical practices. In this article, I address a different, but crucial question: what has made this project of p-medicine convincing to so many? My argument is that without real achievements, it would not have been the case. I also make the case that these achievements stem from the domain of monoclonal antibodies (mab), a new type of drug that prompted talks of personalized, precise, stratified, etc., medicine. In conclusion, while it may be possible to label any projection in the future of medicine as ‘p-medicine’, it is impossible to overlook mab-medicine as the spearhead of current research programs in p-medicine and medical practices that follow.

Precision Medicine has become a common label for data-intensive and patient-driven biomedical research. Its intended future is reflected in endeavours such as the Precision Medicine Initiative in the USA. This article addresses the question whether it is possible to discern a new 'medical cosmology' in Precision Medicine, a concept that was developed by Nicholas Jewson to describe comprehensive transformations involving various dimensions of biomedical knowledge and practice, such as vocabularies, the roles of patients and physicians and the conceptualisation of disease. Subsequently, I will elaborate my assessment of the features of Precision Medicine with the help of Michel Foucault, by exploring how precision medicine involves a transformation along three axes: the axis of biomedical knowledge, of biomedical power and of the patient as a self. Patients are encouraged to become the managers of their own health status, while the medical domain is reframed as a data-sharing community, characterised by changing power relationships between providers and patients, producers and consumers. While the emerging Precision Medicine cosmology may surpass existing knowledge frameworks; it obscures previous traditions and reduces research-subjects to mere data. This in turn, means that the individual is both subjected to the neoliberal demand to share personal information, and at the same time has acquired the positive 'right' to become a member of the data-sharing community. The subject has to constantly negotiate the meaning of his or her data, which can either enable self-expression, or function as a commanding Superego.

Le terme de médecine personnalisee peut designer indifferemment la medecine de precision et la medecine des systemes. L’objectif de cet article est d’analyser les rapports entre ces deux faces de la medecine personnalisee, au prisme de l’histoire du developpement des thérapies ciblées. Les thérapies ciblées ont été développées dans le cadre de la médecine de précision, en s’appuyant sur le concept d’addiction oncogénique et sur l’idée qu’à chaque biomarqueur permettant d’identifier un sous-type de cancer correspondrait un medicament precis. Ce modèle a permis des succès thérapeutiques remarquables, mais a également rencontrédes limites, notamment avec l’apparition des résistances aux thérapies ciblées. Pour mieux comprendre les raisons de ces limites, il me semble qu’il faut justement se tourner plutot vers la médecine des systèmes. Je soutiens en particulier que cette dernière repose sur des concepts de robustesse et de redondance fonctionnelle, qui nous permettent de comprendre en quoi l’apparition des phénomènes de résistance est une conséquence inéluctable et non transitoire du développement des thérapies ciblées. Il s’agit donc de montrer comment la médecine des systèmes permet de penser différemment les stratégies thérapeutiques dans la prise en charge du cancer et l’évolution future de la médecine de précision.

The term ‘personalized’ medicine can alternatively designate either precision medicine or systems medi- cine. The goal of this paper is to analyze the relationships between these two faces of personalized medi- cine, through the development of targeted therapies. Targeted therapies have been developed in the fra- mework of precision medicine, by relying on the concept of oncogenic addiction and on the idea that, for each biomarker allowing the identifiation of a cancer subtype, there would be a specific therapy corresponding. This model has provided great therapeutic successes, but has also met some limitations, especially with the apparition of resistances to targeted therapies. In order to understand these limitations, I believe we should rather turn towards systems medicine. Indeed, I argue that systems medicine relies on concepts of robustness and functional redundancy, that allow us to understand how the apparition of resistance is an inelectuable (and not transient) consequence of the development of targeted therapies. Hence, I aim to show how medicine systems allows to think differently about therapeutic strategies in cancer and the evolution of precision medicine.

During the past two decades the first sequencing of the human genome was performed showing its high degree of inter-individual differentiation, as a result of large international research projects (Human Genome Project, the 1000 Genomes Project International HapMap Project, and Programs for Genomic Applications NHLBI-PGA). This period was also a time of intensive development of molecular biology techniques and enormous knowledge growth in the biology of cancer. For clinical use in the treatment of patients with colorectal cancer (CRC), in addition to fluoropyrimidines, another two new cytostatic drugs were allowed: irinotecan and oxaliplatin. Intensive research into new treatment regimens and a new generation of drugs used in targeted therapy has also been conducted. The last 20 years was a time of numerous in vitro and in vivo studies on the molecular basis of drug resistance. One of the most important factors limiting the effectiveness of chemotherapy is the primary and secondary ...

More than 30% of the world's population are anemic with serious economic consequences including reduced work capacity and other obstacles to national welfare and development. Red blood cell transfusion is the mainstay to correct anemia, but it is also 1 of the top 5 overused procedures. Patient blood management (PBM) is a proactive, patient-centered, and multidisciplinary approach to manage anemia, optimize hemostasis, minimize iatrogenic blood loss, and harness tolerance to anemia. Although the World Health Organization has endorsed PBM in 2010, many hospitals still seek guidance with the implementation of PBM in clinical routine. Given the use of proven change management principles, we propose simple, cost-effective measures enabling any hospital to reduce both anemia and red blood cell transfusions in surgical and medical patients. This article provides comprehensive bundles of PBM components encompassing 107 different PBM measures, divided into 6 bundle blocks acting as a wo...