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.     

Systems Biology,Systems Medicine,Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.     

Infection systems biology: from reactive to proactive (P4) medicine

This short review establishes the conceptual bases and discusses the principal aspects of P4-shorthand for predictive, preventive, personalized and participatory medicine-medicine, in the framework of infectious diseases. P4 medicine is a new way to approach medical care; instead of acting when the patient is sick, physicians will be able to detect early warnings of disease to take early action. Furthermore, people might even be able to adjust their lifestyles to prevent disease. P4 medicine is fuelled by systems approaches to disease, including methods for personalized genome sequencing and new computational techniques for building dynamic disease predictive networks from massive amounts of data from a variety of OMICs. An excellent example of the effectiveness of the P4 medicine approach is the change in cancer treatments. Emphasis is placed on early detection, followed by genotyping of the patient to use the most adequate treatment according to the genetic background. Cardiovascular diseases and perhaps even neurodegenerative disorders will be the next targets for P4 medicine. The application of P4 medicine to infectious diseases is still in its infancy, but is a promising field that will provide much benefit to both the patients and the health-care system.     

军校预防医学专业本科学员临床课程教学现状及建议

随着医学模式的转变,预防医学已经成为现代医疗体系的重要组成部分,在提高公共卫生健康水平方面发挥着越来越重要的作用。为了更好地开展预防医学工作,预防医学专业学员不仅要掌握牢固的预防医学专业知识,更要具备丰富的临床医学知识。针对预防医学专业本科学员的临床课程教学,我校经过多年的探索与改革,已经积累了丰富经验,教学质量较高;但现阶段仍然存在着一些问题。本文分析我校预防医学专业本科学员临床课程的教学现状及存在的主要问题,并提出建议;从而为进一步提高预防医学专业本科学员的临床课程教学质量提供依据。     

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.     

Computational Challenges of Personal Genomics

It is widely predicted that cost and efficiency gains in sequencing will usher in an era of personal genomics and personalized, predictive, preventive, and participatory medicine within a decade. I review the computational challenges ahead and propose general and specific directions for research and development. There is an urgent need to develop semantic ontologies that span genomics, molecular systems biology, and medical data. Although the development of such ontologies would be costly and difficult, the benefits will far outweigh the costs. I argue that availability of such ontologies would allow a revolution in web-services for personal genomics and medicine.     

预防医学本科生在临床实习中存在的问题及其改进建议

预防医学是现代医疗体系的重要组成部分,在提高公共卫生健康水平方面发挥着极其重要的作用。为了更好地开展预防医学工作,预防医学专业本科生不仅要牢固掌握预防医学专业知识,更要掌握一定的临床医学基础知识和技能。临床实习是医学生加深所学的临床理论知识并将其转化为实践操作的主要途径,在预防医学本科生的培养中具有重要意义。我校经过多年的探索与改革,在预防医学本科生的临床实习方面积累了大量经验,但现阶段仍然存在着一些问题。本文分析预防医学本科生在临床实习过程中存在的主要问题并提出改进建议,从而为进一步提高预防医学本科生的培养质量提供依据。     

Predictive diagnostics and personalized medicine for the prevention of chronic degenerative diseases

Progressive increase of mean age and life expectancy in both industrialized and emerging societies parallels an increment of chronic degenerative diseases (CDD) such as cancer, cardiovascular, autoimmune or neurodegenerative diseases among the elderly. CDD are of complex diagnosis, difficult to treat and absorbing an increasing proportion in the health care budgets worldwide. However, recent development in modern medicine especially in genetics, proteomics, and informatics is leading to the discovery of biomarkers associated with different CDD that can be used as indicator of disease's risk in healthy subjects. Therefore, predictive medicine is merging and medical doctors may for the first time anticipate the deleterious effect of CDD and use markers to identify persons with high risk of developing a given CDD before the clinical manifestation of the diseases. This innovative approach may offer substantial advantages, since the promise of personalized medicine is to preserve individual health in people with high risk by starting early treatment or prevention protocols. The pathway is now open, however the road to an effective personalized medicine is still long, several (diagnostic) predictive instruments for different CDD are under development, some ethical issues have to be solved. Operative proposals for the heath care systems are now needed to verify potential benefits of predictive medicine in the clinical practice. In fact, predictive diagnostics, personalized medicine and personalized therapy have the potential of changing classical approaches of modern medicine to CDD.     

The path to personalized medicine

Advances in personalized medicine, or the use of an individual's molecular profile to direct the practice of medicine, have been greatly enabled through human genome research. This research is leading to the identification of a range of molecular markers for predisposition testing, disease screening and prognostic assessment, as well as markers used to predict and monitor drug response. Successful personalized medicine research programs will not only require strategies for developing and validating biomarkers, but also coordinating these efforts with drug discovery and clinical development.     

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.

     

Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine

The emergence of systems biology is bringing forth a new set of challenges for advancing science and technology. Defining ways of studying biological systems on a global level, integrating large and disparate data types, and dealing with the infrastructural changes necessary to carry out systems biology, are just a few of the extraordinary tasks of this growing discipline. Despite these challenges, the impact of systems biology will be far-reaching, and significant progress has already been made. Moving forward, the issue of how to use systems biology to improve the health of individuals must be a priority. It is becoming increasingly apparent that the field of systems biology and one of its important disciplines, proteomics, will have a major role in creating a predictive, preventative, and personalized approach to medicine. In this review, we define systems biology, discuss the current capabilities of proteomics and highlight some of the necessary milestones for moving systems biology and proteomics into mainstream health care.     

Potential role of metabolomics in the improvement of research on traditional African medicine

The global market for herbal medicine is growing steadily. The usage of herbal medicine is particularly common in many parts of Africa; the World Health Organization estimates that approximately 80% of Africans rely on traditional African medicines (TAMs) for treating various diseases. TAMs hold promise in preventive treatment, early disease intervention and personalized medicine. However, clinical integration of TAMs is restricted due to limited information concerning their characterization. Presently, many studies on TAMs utilize a reductionist approach, making it extremely difficult to understand the holistic modifying effects that these therapeutic agents may have on biological systems. Fortunately, emerging technologies such as metabolomics platforms adopt a ⿿top-down⿿ strategy that permits a holistic evaluation of the components, metabolic pathways and biomarkers modified by TAMs, which can aid in addressing common concerns over safety and toxicity, while also ensuring that quality control standards are met. Metabolomics approaches may also be beneficial for advancing our understanding of the efficacy and mechanism of action of TAMs, and may contribute to the advancement of research and drug discovery, early diagnosis, preventive treatment and TAMs-driven personalized medicine in Africa. This review also considers the main challenges that may hinder the adoption and integration of metabolomics approaches in research on TAMs in Africa and suggests possible solutions.     

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.     

Systems approach to identify environmental exposures contributing to organ-specific carcinogenesis

BackgroundThe most effective way to reduce cancer burden is Q2 prevention which is dependent on identifying individuals at risk for a particular cancer and counseling them to avoid exposure to causative agents. Other than a few well characterized environmental agents linked to specific cancers, linkage between any particular environmental exposure and a specific type of cancer is mostly unknown. Thus, we propose a systems approach to analyze publicly available large datasets to identify candidate agents that play a role in organ-specific carcinogenesis.MethodsPublicly available datasets for mRNA and miRNA expression in ovarian cancer were queried to define the differentially expressed genes that are also targets of differentially expressed miRNAs. These target genes were then used to query the Comparative Toxicogenomics Database to identify interacting chemicals and also were analyzed by Ingenuity Pathway Analysis to identify pathways.ResultsThe interacting chemicals interact with genes in known pathways in ovarian carcinogenesis and support the hypothesis that these chemicals are likely etiologic agents in ovarian carcinogenesis.ConclusionA systems approach may prove useful to identify specific etiologic agents to better develop personalized preventive medicine strategies for those most at risk.     

Quantifying your body: A how-to guide from a systems biology perspective

During the coming decade we will see an accelerated digital transformation of healthcare. Leading this change within the institutional medical community are both the move to digital medical records and the use of digital biomedical measurement devices. In addition to this institutional evolution, there is a non-institutional, bottom-up, unorganized, highly idiosyncratic movement by early adopters to "quantify" their own bodies. In this article, I share my decade-long personal experience of tracking many blood and stool biomarkers, which provide insight into the health or disease of major subsystems of my body. These results are interpreted in the context of the genetics of my human DNA and that of the microbes in my gut. Even though I am a computer scientist and not a medical professional, by using commercially available tests and a systems biology integrative approach, I have become an early example of Leroy Hood's vision of the emergence of predictive, preventive, personalized, and participatory (P4) medicine. It is an individual's story illustrating how each of us can contribute to realizing this paradigm shift.     

Editorial: Systems biology and personalized medicine - the future is now

This special issue on "Systems biology and personalized medicine" includes five complementary articles that highlight how functional genomics and computational physiology can contribute to the development of predictive, preventive, personalized and participatory (P4) medicine. Edited by Prof. Leroy Hood and Prof. Charles Auffray.     

Translational disease interpretation with molecular networks

Molecular networks are being used to reconcile genotypes and phenotypes by integrating medical information. In this context, networks will be instrumental for the interpretation of disease at the personalized medicine level.