A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory

Systems biology and the digital revolution are together transforming healthcare to a proactive P4 medicine that is predictive, preventive, personalized and participatory. Systems biology - holistic, global and integrative in approach - has given rise to systems medicine, a systems approach to health and disease. Systems medicine promises to (1) provide deep insights into disease mechanisms, (2) make blood a diagnostic window for viewing health and disease for the individual, (3) stratify complex diseases into their distinct subtypes for a impedance match against proper drugs, (4) provide new approaches to drug target discovery and (5) generate metrics for assessing wellness. P4 medicine, the clinical face of systems medicine, has two major objectives: to quantify wellness and to demystify disease. Patients and consumers will be a major driver in the realization of P4 medicine through their participation in medically oriented social networks directed at improving their own healthcare. P4 medicine has striking implications for society - including the ability to turn around the ever-escalating costs of healthcare. The challenge in bringing P4 medicine to patients and consumers is twofold: first, inventing the strategies and technologies that will enable P4 medicine and second, dealing with the impact of P4 medicine on society - including key ethical, social, legal, regulatory, and economic issues. Managing the societal problems will pose the most significant challenges. Strategic partnerships of a variety of types will be necessary to bring P4 medicine to patients.     

Revolutionizing medicine in the 21(st) century through systems approaches

Personalized medicine is a term for a revolution in medicine that envisions the individual patient as the central focus of healthcare in the future. The term "personalized medicine", however, fails to reflect the enormous dimensionality of this new medicine that will be predictive, preventive, personalized, and participatory-a vision of medicine we have termed P4 medicine. This reflects a paradigm change in how medicine will be practiced that is revolutionary rather than evolutionary. P4 medicine arises from the confluence of a systems approach to medicine and from the digitalization of medicine that creates the large data sets necessary to deal with the complexities of disease. We predict that systems approaches will empower the transition from conventional reactive medical practice to a more proactive P4 medicine focused on wellness, and will reverse the escalating costs of drug development an will have enormous social and economic benefits. Our vision for P4 medicine in 10 years is that each patient will be associated with a virtual data cloud of billions of data points and that we will have the information technology for healthcare to reduce this enormous data dimensionality to simple hypotheses about health and/or disease for each individual. These data will be multi-scale across all levels of biological organization and extremely heterogeneous in type - this enormous amount of data represents a striking signal-to-noise (S/N) challenge. The key to dealing with this S/N challenge is to take a "holistic systems approach" to disease as we will discuss in this article.     

Paradigm shift in natural product research: traditional medicine inspired approaches

The development of traditional medicine with the perspectives of safety, efficacy and quality would help not only to preserve the traditional heritage but also to rationalize the use of herbal medicine in the human healthcare. Nature is considered as a compendium for templates of new chemical entities. The medicinal plants mentioned in the different ancient texts worldwide may be explored with the modern scientific approaches for better leads in the healthcare. Drugs from medicinal plants are unique for their chemical and biological features, and are gaining global acceptance because they offer natural ways to treat diseases and promote healthcare. Natural products are the best sources of chemical diversity for finding new drugs and leads. Globalization of traditional medicine is necessary for health care with assessment of its safety, efficacy, therapeutic and clinical evidence. Evidence based validation of the ethnopharmacological claims on traditional medicine is necessary for its promotion and development. Applications of techniques such as marker analysis, DNA bar coding, plant metabolomics, network pharmacology etc. are being taken into account for the validation and documentation of medicinal plants. This can be achieved by the scientific exploitation of the established facts from ancient systems through proper validation of the claims based on pharmacological and phytochemical assessments.     

Precision medicine in human heart modeling

Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

     

Generalizing genetical genomics: getting added value from environmental perturbation

Genetical genomics is a useful approach for studying the effect of genetic perturbations on biological systems at the molecular level. However, molecular networks depend on the environmental conditions and, thus, a comprehensive understanding of biological systems requires studying them across multiple environments. We propose a generalization of genetical genomics, which combines genetic and sensibly chosen environmental perturbations, to study the plasticity of molecular networks. This strategy forms a crucial step toward understanding why individuals respond differently to drugs, toxins, pathogens, nutrients and other environmental influences. Here we outline a strategy for selecting and allocating individuals to particular treatments, and we discuss the promises and pitfalls of the generalized genetical genomics approach.     

A prospective multiple case study of the impact of emerging scientific evidence on established colorectal cancer screening programs: a study protocol

In this editorial, we reflect on the arguments for starting a scientific society focused on research on how to improve healthcare. This society would take an inclusive approach to what constitutes healthcare. For instance, it should include mental health healthcare, treatment for substance abuse, the work of allied health professions, and preventive healthcare. The society would be open to researchers from all traditions. Thus, we take an inclusive approach to what constitutes scientific research, as long as it uses rigorous methods, is focused on improving healthcare, and aims at knowledge that can be transferred across settings. The society would primarily target scientific researchers but would invite others with an interest in this area of research, regardless of their discipline, position, field of application, or group affiliation (e.g., improvement science, behavioral medicine, knowledge translation). A society would need fruitful collaboration with related societies and organizations, which may include having combined meetings. Special links may be developed with one or more journals. A website to provide information on relevant resources, events, and training opportunities is another key activity. It would also provide a voice for the field at funding agencies, political arenas, and similar institutions. An organizational structure and financial resources are required to develop and run these activities. Our aim is to start an international debate, to discover if we can establish a shared vision across academics and stakeholders engaged with creating scientific knowledge on how to improve healthcare. We invite readers to express their views in the online questionnaire accessed by following the URL link provided at the end of the editorial.     

The Ethical Imperative to Move to a Seven-Day Care Model

Whilst the nature of human illness is not determined by time of day or day of week, we currently structure health service delivery around a five-day delivery model. At least one country is endeavouring to develop a systems-based approach to planning a transition from five- to seven-day healthcare delivery models, and some services are independently instituting program reorganization to achieve these ends as research, amongst other things, highlights increased mortality and morbidity for weekend and after-hours admissions to hospitals. In this article, we argue that this issue does not merely raise instrumental concerns but also opens up a normative ethical dimension, recognizing that clinical ethical dilemmas are impacted on and created by systems of care. Using health policy ethics, we critically examine whether our health services, as currently structured, are at odds with ethical obligations for patient care and broader collective goals associated with the provision of publicly funded health services. We conclude by arguing that a critical health policy ethics perspective applying relevant ethical values and principles needs to be included when considering whether and how to transition from five-day to seven-day models for health delivery.     

Analysing stratified medicine business models and value systems: innovation-regulation interactions

Stratified medicine offers both opportunities and challenges to the conventional business models that drive pharmaceutical R&D. Given the increasingly unsustainable blockbuster model of drug development, due in part to maturing product pipelines, alongside increasing demands from regulators, healthcare providers and patients for higher standards of safety, efficacy and cost-effectiveness of new therapies, stratified medicine promises a range of benefits to pharmaceutical and diagnostic firms as well as healthcare providers and patients. However, the transition from 'blockbusters' to what might now be termed 'niche-busters' will require the adoption of new, innovative business models, the identification of different and perhaps novel types of value along the R&D pathway, and a smarter approach to regulation to facilitate innovation in this area. In this paper we apply the Innogen Centre's interdisciplinary ALSIS methodology, which we have developed for the analysis of life science innovation systems in contexts where the value creation process is lengthy, expensive and highly uncertain, to this emerging field of stratified medicine. In doing so, we consider the complex collaboration, timing, coordination and regulatory interactions that shape business models, value chains and value systems relevant to stratified medicine. More specifically, we explore in some depth two convergence models for co-development of a therapy and diagnostic before market authorisation, highlighting the regulatory requirements and policy initiatives within the broader value system environment that have a key role in determining the probable success and sustainability of these models.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

Towards a systems level analysis of health and nutrition

Although theoretical systems analysis has been available for over half a century, the recent advent of omic high-throughput analytical platforms along with the integration of individual tools and technologies has given rise to the field of modern systems biology. Coupled with information technology, bioinformatics, knowledge management and powerful mathematical models, systems biology has opened up new vistas in our understanding of complex biological systems. Currently there are two distinct approaches that include the inductively driven computational systems biology (bottom-up approach) and the deductive data-driven top-down analysis. Such approaches offer enormous potential in the elucidation of disease as well as defining key pathways and networks involved in optimal human health and nutrition. The tools and technologies now available in systems biology analyses offer exciting opportunities to develop the emerging areas of personalized medicine and individual nutritional profiling.     

Theme C: Medical information systems and databases – results and future work

This paper presents the activities of the theme C “medical information systems and databases” in the GDR Stic Santé. Six one-day workshops have been organized during the period 2011–2012. They were devoted to 1) sharing anatomical and physiological object models for simulation of clinical medical images, 2) advantages and limitations of datawarehouse for biological data, 3) medical information engineering, 4) systems for sharing medical images for research, 5) knowledge engineering for semantic interoperability in e-health applications, and 6) using context in health. In the future, our activities will continue with a specific interest on information systems for translational medicine and the role of electronic healthcare reports in decision-making. Workshops with other research groups will be organized in particular with the e-health research group.     

Translational genomics in personalized medicine – scientific challenges en route to clinical practice

Background

In the area of omics and translational bio(medical)sciences, there is an increasing need to integrate, normalize, analyze, store and protect genomics data. Large datasets and scientific knowledge are rationally combined into valuable clinical information that ultimately will benefit human healthcare and are en route to clinical practice. Data from biomarker discovery and Next Generation Sequencing (NGS) are very valuable and will combine in comprehensive analyses to stratify medicine and guide therapy planning and ultimately benefit patients. However, the combination into useful and applicable information and knowledge is not trivial.

NGS in personalized medicine

Personalized medicine generally promises to result in both higher quality in treatment for individual patients and in lower costs in health care since patients will be offered only such therapies that are more effective for them and treatments that will not be safe or effective will be avoided. Recent advancements in biomedical and genomic sciences have paved the way to translate such research into clinical practice and health policies. However, the move towards greater personalization of medicine also comes along with challenges in the development of novel diagnostic and therapeutic tools in a complex framework that assumes that the use of genomic information is part of a translational continuum, which spans from basic to clinical research, preclinical and clinical trials, to policy research and the analysis of health and economic outcomes. The use of next-generation genomic technologies to improve the quality of life and efficiency of healthcare delivered to patients has become a mainstay theme in the field as benefits derived from such approaches include reducing a patient’s need to go through ineffective therapies, lowering side- and off-target effects of drugs, prescribing prophylactic therapies before acute exacerbations, and reducing expenditures.

Economic challenges

As such, personalized medicine promises to increase the quality of clinical care and, in some cases, to decrease health care costs. Besides the scientific challenges, there are several economic hurdles. For instance, healthcare providers need to know, whether the approach of personalized healthcare is affordable and worth the expenses. In addition, the economic rationale of personalized healthcare includes not only the reduction of the high expense of hospitalizations, the predictive diagnostics that will help to reduce cost through prevention or the increased efficacy of personalized therapies needs to offset prices of drugs. There are also several factors that influence payer adoption, coverage and reimbursement; the strength of evidence drives payers‘ decisions about coverage and reimbursement, varies widely depending on the personalized healthcare technology applied and regulation and cost-effectiveness seem to be increasingly associated with reimbursement, which is strongly influenced by professional society guidelines. In general, we see the following main obstacles to the advancement of personalized medicine: (i) the scientific challenges (a poor understanding of molecular mechanisms or a lack of molecular markers associated with some diseases, for example), (ii) the economic challenges (poorly aligned incentives), and (iii) operational issues in public healthcare systems. The operational issues can often be largely resolved within a particular stakeholder group, but correcting the incentive structure and modifying the relationships between stakeholders is more complex.

En route to clinical practice

This article focuses on the scientific difficulties that remain to translate genomics technologies into clinical practice and reviews recent technological advances in genomics and the challenges and potential benefits of translating this knowledge into clinical practice, with a particular focus on their applications in oncology.

Electronic supplementary material

The online version of this article (doi:10.1186/1877-6566-6-2) contains supplementary material, which is available to authorized users.     

Exploring and exploiting disease interactions from multi-relational gene and phenotype networks

The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.     

The Assimilation of Western Medicine into a Semi-nomadic Healthcare System: A Case Study of the Indigenous Aeta Magbukún,Philippines

The Aeta Magbukún are a genetically and culturally distinct group of Indigenous people living in an isolated mountain forest in the municipality of Mariveles, in the province of Bataan, Philippines. This research aims to document some healthcare related information of the people, inform future decisions regarding maximising benefits of modern conveniences, and minimise negative consequences on their culture and health. Using an ethnographic approach, data were collated from a community health survey in combination with field notes from three of the co-authors while living with the Aetas. Despite major implications from rapid ecological and cultural changes, traditional ethnomedical systems continue to be revered as an essential healing practice, although they are increasingly used in conjunction with Western medicines and healthcare. At the Aeta village level, the changing socio-political influence among the kagun (traditional healer), the NGOs, and the Municipal Council in terms of healthcare provision is pivotal, as the kagun has chosen to integrate the Western medicine and healthcare services into their traditional healthcare system, without simply rejecting them. In turn, Western-style healthcare interventions have the potential to be carefully managed to integrate traditional Aeta Magbukún socio-political structures, healthcare, and cultural continuity. The cumulative influence of numerous other novel aspects to Aeta life (e.g., permanent housing, a highway through the village, literacy, cash economies, energy-dense foods, communication/entertainment devices, etc.) will place additional pressure on the traditional ethnomedical healthcare system. However, enabling the continuity of access to appropriate healthcare knowledge (both the transfer of knowledge from Western medicine to the Aeta Magbukún, and vice versa) can assist many cultures through the inherent stresses of increasingly rapid acculturation and development.     

Applications of a formal approach to decipher discrete genetic networks

Background  

A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language.     

An economic perspective on personalized medicine

The concept of personalized medicine not only promises to enhance the life of patients and increase the quality of clinical practice and targeted care pathways, but also to lower overall healthcare costs through early-detection, prevention, accurate risk assessments and efficiencies in care delivery. Current inefficiencies are widely regarded as substantial enough to have a significant impact on the economies of major nations like the US and China, and, therefore the world economy. A recent OECD report estimates healthcare expenditure for some of the developed western and eastern nations to be anywhere from 10% to 18%, and growing (with the US at the highest). Personalized medicine aims to use state-of-the-art genomic technologies, rich medical record data, tissue and blood banks and clinical knowledge that will allow clinicians and payors to tailor treatments to individuals, thereby greatly reducing the costs of ineffective therapies incurred through the current trial and error clinical paradigm. Pivotal to the field are drugs that have been designed to target a specific molecular pathway that has gone wrong and results in a diseased condition and the diagnostic tests that allow clinicians to separate responders from non-responders. However, the truly personalized approach in medicine faces two major problems: complex biology and complex economics; the pathways involved in diseases are quite often not well understood, and most targeted drugs are very expensive. As a result of all current efforts to translate the concepts of personalized healthcare into the clinic, personalized medicine becomes participatory and this implies patient decisions about their own health. Such a new paradigm requires powerful tools to handle significant amounts of personal information with the approach to be known as “P4 medicine”, that is predictive, preventive, personalized and participatory. P4 medicine promises to increase the quality of clinical care and treatments and will ultimately save costs. The greatest challenges are economic, not scientific.     

The systems biology of signaling pathways

Signaling networks play the central role in the regulation of processes in a single cell and in the entire body. A recent breakthrough in technologies for systems biology, which combine experimental and mathematical methods, permits scientists to model signaling pathways in an individual cell and in cell populations. This approach provides new information on mechanisms that regulate a variety of biological processes. Here we discuss the mathematical formalisms that are applied to signaling pathway modeling and relevant experimental methods.     

The Scale of Faith Based Organization Participation in Health Service Delivery in Developing Countries: Systemic Review and Meta-Analysis

The extent of faith-based organizations'' participation within the overall health systems of developing countries is unclear. Recent reports state that faith-based organizations play a substantial role in providing healthcare in developing countries, cited in some publications as up to 70% of all healthcare services. The data behind these numbers are sometimes difficult to pinpoint and seem at odds to national and regional survey data. In an effort to quantify the contribution of faith-based organizations to healthcare delivery in low- and middle-income countries, we undertook a systematic review of the literature and conducted a new analysis of relevant Demographic and Health Survey data from 47 countries. Our findings demonstrate that the magnitude of healthcare provided by faith-based organizations may be lower than previously estimated. Understanding the scale of FBO-provided medical care is important for health sector planning, and more accurate and complete estimates are needed.     

Health in China. Traditional Chinese medicine: one country,two systems.

China is the only country in the world where Western medicine and traditional medicine are practised alongside each other at every level of the healthcare system. Traditional Chinese medicine has a unique theoretical and practical approach to the treatment of disease, which has developed over thousands of years. Traditional treatments include herbal remedies, acupuncture, acupressure and massage, and moxibustion. They account for around 40% of all health care delivered in China. The current government policy of expansion of traditional facilities and manpower is being questioned because many hospitals using traditional Chinese medicine are already underutilized and depend on government subsidies for survival. Research priorities include randomised controlled trials of common treatments and analysis of the active agents in herbal remedies. As more studies show the clinical effectiveness of traditional Chinese medicine, an integrated approach to disease using a combination of Western medicine and traditional approaches becomes a possibility for the future.