Conceptual and terminological confusion around personalised medicine: a coping strategy

The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Representations of personalised medicine in family medicine: a qualitative analysis

Background

The promise of personalised medicine (PM) to transform healthcare has sparked great enthusiasm in the last years. Yet, its lack of consensus around the nature and scope of the concept has ended in terminological confusion amongst the users in primary care. We aimed to investigate the perceptions of doctors and their patients in response to this evolving concept. This present article focuses on the general understanding of personalised medicine, underlining the confusion over the concept.

Methods

Semi-structured comprehensive interviews were conducted with 10 general practitioners (GPs) and 10 of their patients. The purposive sampling took into account the doctor’s age, sex, and place of practice (rural/urban); each doctor recruited one patient of the same age and sex. Each interview began with the same open-ended question about the participant’s knowledge of the topic, after which a working definition was provided to continue the discussion. Using the grounded theory method, the analysis consisted of open coding, axial coding and selective coding.

Results

From our present analysis focusing on the general understanding of PM, three main themes representing the concept emerged. The first two representations being “centred on the person as a whole” and “focused on alternative and complementary methods”, in which the therapeutic relationship was stated as key. The third theme “medicine open to innovation” involved the few participants who had a good understanding of the concept and could associate personalised medicine with genomics. For those who value therapeutic relationship, the risks of accepting innovation could result in “fast-food” medicine and interpersonal barriers.

Discussion

PM is predominantly unfamiliar in family medicine. It is misinterpreted as a holistic or integrative type of medicine. This semantic confusion probably lies in the choice of the label “personalised” or from the lack of a uniform definition for the term.

The current understanding of precision medicine and personalised medicine in selected research disciplines: study protocol of a systematic concept analysis

INTRODUCTION

The terms 'precision medicine' and 'personalised medicine' have become key terms in health-related research and in science-related public communication. However, the application of these two concepts and their interpretation in various disciplines are heterogeneous, which also affects research translation and public awareness. This leads to confusion regarding the use and distinction of the two concepts. Our aim is to provide a snapshot of the current understanding of these concepts.

METHODS AND ANALYSIS

Our study will use Rodgers' evolutionary concept analysis to systematically examine the current understanding of the concepts 'precision medicine' and 'personalised medicine' in clinical medicine, biomedicine (incorporating genomics and bioinformatics), health services research, physics, chemistry, engineering, machine learning and artificial intelligence, and to identify their respective attributes (clusters of characteristics) and surrogate and related terms. A systematic search of the literature will be conducted for 2016-2022 using databases relevant to each of these disciplines: ACM Digital Library, CINAHL, Cochrane Library, F1000Research, IEEE Xplore, PubMed/Medline, Science Direct, Scopus and Web of Science. These are among the most representative databases for the included disciplines. We will examine similarities and differences in definitions of 'precision medicine' and 'personalised medicine' in the respective disciplines and across (sub)disciplines, including attributes of each term. This will enable us to determine how these two concepts are distinguished.

ETHICS AND DISSEMINATION

Following ethical and research standards, we will comprehensively report the methodology for a systematic analysis following Rodgers' concept analysis method. Our systematic concept analysis will contribute to the clarification of the two concepts and distinction in their application in given settings and circumstances. Such a broad concept analysis will contribute to non-systematic syntheses of the concepts, or occasional systematic reviews on one of the concepts that have been published in specific disciplines, in order to facilitate interdisciplinary communication, translational medical research and implementation science.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

The prospects of precision psychiatry

Since the turn of the twenty-first century, biomedical psychiatry around the globe has embraced the so-called precision medicine paradigm, a model for medical research that uses innovative techniques for data collection and analysis to reevaluate traditional theories of disease. The goal of precision medicine is to improve diagnostics by restratifying the patient population on the basis of a deeper understanding of disease processes. This paper argues that precision is ill-fitting for psychiatry for two reasons. First, in psychiatry, unlike in fields like oncology, precision medicine has been understood as an attempt to improve medicine by casting out, rather than merely revising, traditional taxonomic tools. Second, in psychiatry the term "biomarker" is often used in reference to signs or symptoms that allow patients to be classified and then matched with treatments; however, in oncology "biomarker" usually refers to a disease mechanism that is useful not only for diagnostics, but also for discovering causal pathways that drug therapies can target. Given these differences between how the precision medicine paradigm operates in psychiatry and in other medical fields like oncology, while precision psychiatry may offer successful rhetoric, it is not a promising paradigm.

Milestones of Precision Medicine: An Innovative, Multidisciplinary Overview

Although the concept of precision medicine, in which healthcare is tailored to the molecular and clinical characteristics of each individual, is not new, its implementation in clinical practice has been heterogenous. In some medical specialties, precision medicine has gone from being just a promise to a reality that achieves better patient outcomes. This is a fact if we consider, for example, the great advances made in the genetic diagnosis and subsequent treatment of countless hereditary diseases, such as cystic fibrosis, which have improved the life expectancy of many of the affected children. In the field of oncology, the development of targeted therapies has prolonged the survival of patients with breast, lung, colorectal, melanoma, and hematological malignancies. In other disciplines, clinical milestones are perhaps less well known, but no less important. The current challenge is to expand and generalize the use of technologies that are central to precision medicine, such as massively parallel sequencing, to improve the management (prevention and treatment) of complex conditions such as cardiovascular, kidney, or autoimmune diseases. This process requires investment in specialized expertise, multidisciplinary collaboration, and the nationwide organization of genetic laboratories for diagnosis of specific diseases.

Precision Medicine in Hematology 2021: Definitions, Tools, Perspectives, and Open Questions

During the past few years, our understanding of molecular mechanisms and cellular interactions relevant to malignant blood cell disorders has improved substantially. New insights include a detailed knowledge about disease-initiating exogenous factors, endogenous (genetic, somatic, epigenetic) elicitors or facilitators of disease evolution, and drug actions and interactions that underlie efficacy and adverse event profiles in defined cohorts of patients. As a result, precision medicine and personalized medicine are rapidly growing new disciplines that support the clinician in making the correct diagnosis, in predicting outcomes, and in optimally selecting patients for interventional therapies. In addition, precision medicine tools are greatly facilitating the development of new drugs, therapeutic approaches, and new multiparametric prognostic scoring models. However, although the emerging roles of precision medicine and personalized medicine in hematology and oncology are clearly visible, several questions remain. For example, it remains unknown how precision medicine tools can be implemented in healthcare systems and whether all possible approaches are also affordable. In addition, there is a need to define terminologies and to relate these to specific and context-related tools and strategies in basic and applied science. To discuss these issues, a working conference was organized in September 2019. The outcomes of this conference are summarized herein and include a proposal for definitions, terminologies, and applications of precision and personalized medicine concepts and tools in hematologic neoplasms. We also provide proposals aimed at reducing costs, thereby making these applications affordable in daily practice.

Genetic Signatures of Drug Response Variability in Drosophila melanogaster

Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases.

Data as promise: Reconfiguring Danish public health through personalized medicine

'Personalized medicine' might sound like the very antithesis of population science and public health, with the individual taking the place of the population. However, in practice, personalized medicine generates heavy investments in the population sciences - particularly in data-sourcing initiatives. Intensified data sourcing implies new roles and responsibilities for patients and health professionals, who become responsible not only for data contributions, but also for responding to new uses of data in personalized prevention, drawing upon detailed mapping of risk distribution in the population. Although this population-based 'personalization' of prevention and treatment is said to be about making the health services 'data-driven', the policies and plans themselves use existing data and evidence in a very selective manner. It is as if data-driven decision-making is a promise for an unspecified future, not a demand on its planning in the present. I therefore suggest interrogating how 'promissory data' interact with ideas about accountability in public health policies, and also with the data initiatives that the promises bring about. Intensified data collection might not just be interesting for what it allows authorities to do and know, but also for how its promises of future evidence can be used to postpone action and sidestep uncomfortable knowledge in the present.

Contested futures: envisioning "Personalized," "Stratified," and "Precision" medicine

In recent years, discourses around "personalized," "stratified," and "precision" medicine have proliferated. These concepts broadly refer to the translational potential carried by new data-intensive biomedical research modes. Each describes expectations about the future of medicine and healthcare that data-intensive innovation promises to bring forth. The definitions and uses of the concepts are, however, plural, contested and characterized by diverse ideas about the kinds of futures that are desired and desirable. In this paper, we unpack key disputes around the "personalized," "stratified," and "precision" terms, and map the epistemic, political and economic contexts that structure them as well as the different roles attributed to patients and citizens in competing future imaginaries. We show the ethical and value baggage embedded within the promises that are manufactured through terminological choices and argue that the context and future-oriented nature of these choices helps to understanding how data-intensive biomedical innovations are made socially meaningful.

Regulatory sciences and translational pharmacogenetics: amitriptyline as a case in point

Regulatory developments and clinical implementation, or the lack thereof, are primary clinchers, in the enduring endeavors to realize the translational quality of pharmacogenetics. Here, we present the case of amitriptyline, an established drug with pharmacogenetic implications. The integration of pharmacogenetic information in the official product literature and throughout the evaluation of safety concerns is considered. In our opinion, apart from emboldening genomic research in drug development and the valid pursuit towards global harmonization in the field, it is rational to look into the applicability of the data we have today.

Plastic diagnostics: The remaking of disease and evidence in personalized medicine

Politically authorized reports on personalized and precision medicine stress an urgent need for finer-grained disease categories and faster taxonomic revision, through integration of genomic and phenotypic data. Developing a data-driven taxonomy is, however, not as simple as it sounds. It is often assumed that an integrated data infrastructure is relatively easy to implement in countries that already have highly centralized and digitalized health care systems. Our analysis of initiatives associated with the Danish National Genome Center, recently launched to bring Denmark to the forefront of personalized medicine, tells a different story. Through a “meta-taxonomy” of taxonomic revisions, we discuss what a genomics-based disease taxonomy entails, epistemically as well as organizationally. Whereas policy reports promote a vision of seamless data integration and standardization, we highlight how the envisioned strategy imposes significant changes on the organization of health care systems. Our analysis shows how persistent tensions in medicine between variation and standardization, and between change and continuity, remain obstacles for the production as well as the evaluation of genomics-based taxonomies of difference. We identify inherent conflicts between the ideal of dynamic revision and existing regulatory functions of disease categories in, for example, the organization and management of health care systems. Moreover, we raise concerns about shifts in the regulatory regime of evidence standards, where clinical care increasingly becomes a vehicle for biomedical research.

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Personalized medicine has led to calls for speedy revisions of disease taxonomies.

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A “meta-taxonomy” of disease taxonomic revisions is presented and discussed.

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Fine-graining disease categories has regulatory implications for healthcare systems.

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A Danish case illustrates difficulties in aligning multiple functions of disease codes.

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We argue that the political call for speed should be treated with caution.

Evidence for personalised medicine: mechanisms, correlation, and new kinds of black box

Personalised medicine (PM) has been discussed as a medical paradigm shift that will improve health while reducing inefficiency and waste. At the same time, it raises new practical, regulatory, and ethical challenges. In this paper, we examine PM strategies epistemologically in order to develop capacities to address these challenges, focusing on a recently proposed strategy for developing patient-specific models from induced pluripotent stem cells (iPSCs) so as to make individualised treatment predictions. We compare this strategy to two main PM strategies-stratified medicine and computational models. Drawing on epistemological work in the philosophy of medicine, we explain why these two methods, while powerful, are neither truly personalised nor, epistemologically speaking, novel strategies. Both are forms of correlational black box. We then argue that the iPSC models would count as a new kind of black box. They would not rely entirely on mechanistic knowledge, and they would utilise correlational evidence in a different way from other strategies-a way that would enable personalised predictions. In arguing that the iPSC models would present a novel method of gaining evidence for clinical practice, we provide an epistemic analysis that can help to inform the practical, regulatory, and ethical challenges of developing an iPSC system.

Improving Community Advisory Board Engagement in Precision Medicine Research to Reduce Health Disparities

Community Advisory Boards (CABs) are used in efforts to reduce health disparities; however, there is little documentation in the literature regarding their use in precision medicine research. In this case study, an academic-CAB partnership developed a questionnaire and patient educational materials for two precision smoking cessation interventions that involved use of genetic or genetically-informed information. The community-engaged research (CEnR) literature provided a framework for enhancing benefits to CAB members involved in developing research documents for use with a low-income, ethnically diverse population of smokers.The academic partners integrated three CEnR strategies: 1) in-meeting statements acknowledging their desire to learn from community partners, 2) in-meeting written feedback to and from community partners, and 3) a survey to obtain CAB member feedback post-meetings. Strategies 1 and 2 yielded modifications to pertinent study materials, as well as suggestions for improving meeting operations that were then adopted, as appropriate, by the academic partners. The survey indicated that CAB members valued the meeting procedure changes which appeared to have contributed to improvements in attendance and satisfaction with the meetings. Further operationalization of relevant partnership constructs and development of tools for measuring these aspects of community-academic partnerships is warranted to support community engagement in precision medicine research studies.

I've Gut A Feeling: Microbiota Impacting the Conceptual and Experimental Perspectives of Personalized Medicine

In recent years, the human microbiota has gained increasing relevance both in research and clinical fields. Increasing studies seem to suggest the centrality of the microbiota and its composition both in the development and maintenance of what we call "health" and in generating and/or favoring (those cases in which the microbiota's complex relational architecture is dysregulated) the onset of pathological conditions. The complex relationships between the microbiota and human beings, which invest core notions of biomedicine such as "health" and "individual," do concern not only problems of an empirical nature but seem to require the need to adopt new concepts and new perspectives in order to be properly analysed and utilized, especially for their therapeutic implementation. In this contribution we report and discuss some of the theoretical proposals and innovations (from the ecological component to the notion of polygenomic organism) aimed at producing this change of perspective. In conclusion, we summarily analyze what impact and what new challenges these new approaches might have on personalized/person centred/precision medicine.

Applying systems biology to biomedical research and health care: a précising definition of systems medicine

BACKGROUND

Systems medicine has become a key word in biomedical research. Although it is often referred to as P4-(predictive, preventive, personalized and participatory)-medicine, it still lacks a clear definition and is open to interpretation. This conceptual lack of clarity complicates the scientific and public discourse on chances, risks and limits of Systems Medicine and may lead to unfounded hopes. Against this background, our goal was to develop a sufficiently precise and widely acceptable definition of Systems Medicine.

METHODS

In a first step, PubMed was searched using the keyword "systems medicine". A data extraction tabloid was developed putting forward a means/ends-division. Full-texts of articles containing Systems Medicine in title or abstract were screened for definitions. Definitions were extracted; their semantic elements were assigned as either means or ends. To reduce complexity of the resulting list, summary categories were developed inductively. In a second step, we applied six criteria for adequate definitions (necessity, non-circularity, non-redundancy, consistency, non-vagueness, and coherence) to these categories to derive a so-called précising definition of Systems Medicine.

RESULTS

We identified 185 articles containing the term Systems Medicine in title or abstract. 67 contained at least one definition of Systems Medicine. In 98 definitions, we found 114 means and 132 ends. From these we derived the précising definition: Systems Medicine is an approach seeking to improve medical research (i.e. the understanding of complex processes occurring in diseases, pathologies and health states as well as innovative approaches to drug discovery) and health care (i.e. prevention, prediction, diagnosis and treatment) through stratification by means of Systems Biology (i.e. data integration, modeling, experimentation and bioinformatics). Our study also revealed the visionary character of Systems Medicine.

CONCLUSIONS

Our insights, on the one hand, allow for a realistic identification of actual ethical as well as legal issues arising in the context of Systems Medicine and, in consequence, for a realistic debate of questions concerning its matter and (future) handling. On the other hand, they help avoiding unfounded hopes and unrealistic expectations. This especially holds for goals like improving patient participation which are intensely debated in the context of Systems Medicine, however not implied in the concept.