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.     

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.     

Personalized medicine: revolutionizing drug discovery and patient care.

Advances in human genome research are opening the door to a new paradigm for practising medicine that promises to transform healthcare. Personalized medicine, the use of marker-assisted diagnosis and targeted therapies derived from an individual's molecular profile, will impact the way drugs are developed and medicine is practiced. Knowledge of the molecular basis of disease will lead to novel target identification, toxicogenomic markers to screen compounds and improved selection of clinical trial patients, which will fundamentally change the pharmaceutical industry. The traditional linear process of drug discovery and development will be replaced by an integrated and heuristic approach. In addition, patient care will be revolutionized through the use of novel molecular predisposition, screening, diagnostic, prognostic, pharmacogenomic and monitoring markers. Although numerous challenges will need to be met to make personalized medicine a reality, with time, this approach will replace the traditional trial-and-error practice of medicine.     

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.

     

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.     

A novel classification of lung cancer into molecular subtypes

The remarkably heterogeneous nature of lung cancer has become more apparent over the last decade. In general, advanced lung cancer is an aggressive malignancy with a poor prognosis. The discovery of multiple molecular mechanisms underlying the development, progression, and prognosis of lung cancer, however, has created new opportunities for targeted therapy and improved outcome. In this paper, we define "molecular subtypes" of lung cancer based on specific actionable genetic aberrations. Each subtype is associated with molecular tests that define the subtype and drugs that may potentially treat it. We hope this paper will be a useful guide to clinicians and researchers alike by assisting in therapy decision making and acting as a platform for further study. In this new era of cancer treatment, the 'one-size-fits-all' paradigm is being forcibly pushed aside-allowing for more effective, personalized oncologic care to emerge.     

Engineering antibodies and proteins for molecular in vivo imaging

The rapid and ongoing discovery of new disease related biomarkers leads to a dramatic paradigm change in human healthcare and constitutes the basis for a truly personalized medicine. Molecular imaging enables early detection and classification of human diseases and provides valuable data for optimized, target-oriented therapies. By now, the biochemical and physiological properties of antibody derivatives or alternative protein scaffolds can be engineered for the detection of a wide range of target structures. The successful application of these reagents in animals, xenograft models and cells in preclinical research clearly demonstrate their utility for molecular imaging. Despite these promising perspectives, only a few antibodies and recombinant proteins are used yet for molecular imaging in human medicine. Especially the high safety demands and the need to eliminate off target effects in humans require extensive research and development efforts.     

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.     

Pathology,proteomics and the pathway to personalised medicine

Introduction: As we move from a discovery to a translational phase in proteomics, with a focus on developing validated clinical assays to assist personalized medicine, there is a growing need for strong bidirectional interactions with the clinical pathology community. Thus, while on one hand the proteomics community will provide candidate biomarkers to assist in diagnosis, prognosis, surveillance, identification of individualized patient medication, and development and validation of new assays for diagnostic use, on the other the pathology community will assist with specific tissue identification and selection (e.g. laser capture microdissection, tissue sections for MS imaging), biobanking, validation of emerging automated histopathology techniques, preparation and classification of relevant patient medical reports, and assisting with the optimization of experimental design for clinical trials.

Areas covered: Here we discuss these topics with a particular emphasis on recent publications and relevant initiatives and outline some of the hurdles that still remain for personalized medicine.

Expert commentary: It is clear that effective crosstalk between the proteomics and pathology communities will greatly accelerate crossover of candidate biomarkers to personalized medicine, which will have significant benefits not only for patient wellbeing, but also the global healthcare budget. However, analysis of the big data generated may become rate-limiting.     

Promoting synergistic research and education in genomics and bioinformatics

Bioinformatics and Genomics are closely related disciplines that hold great promises for the advancement of research and development in complex biomedical systems, as well as public health, drug design, comparative genomics, personalized medicine and so on. Research and development in these two important areas are impacting the science and technology.High throughput sequencing and molecular imaging technologies marked the beginning of a new era for modern translational medicine and personalized healthcare. The impact of having the human sequence and personalized digital images in hand has also created tremendous demands of developing powerful supercomputing, statistical learning and artificial intelligence approaches to handle the massive bioinformatics and personalized healthcare data, which will obviously have a profound effect on how biomedical research will be conducted toward the improvement of human health and prolonging of human life in the future. The International Society of Intelligent Biological Medicine (http://www.isibm.org) and its official journals, the International Journal of Functional Informatics and Personalized Medicine (http://www.inderscience.com/ijfipm) and the International Journal of Computational Biology and Drug Design (http://www.inderscience.com/ijcbdd) in collaboration with International Conference on Bioinformatics and Computational Biology (Biocomp), touch tomorrow's bioinformatics and personalized medicine throughout today's efforts in promoting the research, education and awareness of the upcoming integrated inter/multidisciplinary field. The 2007 international conference on Bioinformatics and Computational Biology (BIOCOMP07) was held in Las Vegas, the United States of American on June 25-28, 2007. The conference attracted over 400 papers, covering broad research areas in the genomics, biomedicine and bioinformatics. The Biocomp 2007 provides a common platform for the cross fertilization of ideas, and to help shape knowledge and scientific achievements by bridging these two very important disciplines into an interactive and attractive forum. Keeping this objective in mind, Biocomp 2007 aims to promote interdisciplinary and multidisciplinary education and research. 25 high quality peer-reviewed papers were selected from 400+ submissions for this supplementary issue of BMC Genomics. Those papers contributed to a wide-range of important research fields including gene expression data analysis and applications, high-throughput genome mapping, sequence analysis, gene regulation, protein structure prediction, disease prediction by machine learning techniques, systems biology, database and biological software development. We always encourage participants submitting proposals for genomics sessions, special interest research sessions, workshops and tutorials to Professor Hamid R. Arabnia (hra@cs.uga.edu) in order to ensure that Biocomp continuously plays the leadership role in promoting inter/multidisciplinary research and education in the fields. Biocomp received top conference ranking with a high score of 0.95/1.00. Biocomp is academically co-sponsored by the International Society of Intelligent Biological Medicine and the Research Laboratories and Centers of Harvard University--Massachusetts Institute of Technology, Indiana University--Purdue University, Georgia Tech--Emory University, UIUC, UCLA, Columbia University, University of Texas at Austin and University of Iowa etc. Biocomp--Worldcomp brings leading scientists together across the nation and all over the world and aims to promote synergistic components such as keynote lectures, special interest sessions, workshops and tutorials in response to the advances of cutting-edge research.     

Measuring the performance of international genomics research projects in fostering genomic capacity in the developing world

Therapeutic applications of genomic medicine are slowly finding their way into the healthcare framework of developing countries. The establishment of equitable innovation policies is a determining factor in how genomic-based therapeutic applications will evolve in these countries. In the biomedical field, the commercialization of research results has established itself as the dominant paradigm in the innovation system. However, many recent studies have demonstrated that this emphasis on commercialization and the protection of intellectual property has led to disappointing results. A growing number of stakeholders in this debate argue that it is now necessary to go beyond the commercialization of research and implement policies based on the research valorization paradigm, which supports the achievement of social as well as economic objectives. We thus propose a new set of more inclusive research performance indicators to help policymakers measure the impact of international genomics projects on developing countries.     

Designing Graduate Training Programs in Conservation Medicine—Producing the Right Professionals with the Right Tools

New challenges to human, animal, and ecosystem health demand novel solutions: New diseases are emerging from new configurations of humans, their domestic animals and wildlife; new pressures on once robust and resilient ecosystems are compromising their integrity; synthetic compounds and engineered organisms, new to the natural world, are spreading unpredictably around the globe. Globalization provides opportunities for infectious organisms to gain access to new hosts, changing in distribution and virulence. What type of training should be developed to provide professionals with the right tools to meet these challenges? In this article, we offer recommendations for developing academic programs in conservation medicine. We discuss the need for, and the advantages to, using a conservation medicine approach to address real world situations and present illustrations of how this is applied today. We suggest a core set of skills that are needed in a conservation medicine practitioner, and recommend key considerations for designing new conservation medicine training programs. We review existing programs that offer conservation medicine content, and provide examples of where opportunities exist for those interested in pursuing a conservation medicine career.     

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.     

Practical perspectives of personalized healthcare in oncology

There is an increasing prevalence of drug-diagnostic combinations in oncology. This has placed diagnostic stakeholders directly into the complex benefit-risk, cost, value and uncertainty-driven development paradigm traditionally the preserve of the drug development community. In this review we focus on the delivery of the clinical data required to advance such drug-diagnostic combination development programmes and ultimately satisfy regulators and payors of the value of contemporaneous changes in diagnostic and treatment practice. Ideally all stakeholders would like to initially estimate, and ultimately specify, the comparative benefit-risk for a new treatment option with and without changing diagnostic practice. Hence, in an ideal world clinical trial design is focused on acquiring biomarker treatment interaction data. In this review we describe the key scientific and feasibility inputs required to design and deliver such trials and the drivers, advantages and disadvantages associated with departing from this model. We do not discuss the discovery of new biomarkers nor the analytical validation and marketing of diagnostic products. Following on from trial design we describe how subsequent success then depends upon the concepts that guide trial design being driven into the complex world of large, multinational clinical trial delivery. For every aspect of a traditional clinical drug trial such as supply, recruitment and adherence, there is a corresponding concept for the diagnostic element. In practice, this means that each patient's contribution to the decision making data-set is subject to double jeopardy (attrition on clinical outcome and biomarker status). Historically, this has led to significantly reduced power for detecting biomarker-treatment interactions, reduced decision making confidence and a waste of valuable human and financial resources. We describe recent practice changes and experience that have led to the successful delivery of such trials focusing on both pre- and on trial aspects. The former includes the pivotal role of tissue banks in accurate estimation of evaluability and prevalence for biomarker assays and the latter several practices designed to engage and incentivize key stakeholders particularly CRAs and pathologists. The result is that in the new world of developing personalized treatments for cancer patients the real-time acquisition and monitoring of biomarker data receives similar support to that traditionally reserved for clinical outcome data and far more patients contribute to the testing of personalized medicine hypotheses.     

New approaches to molecular cancer therapeutics

Cancer drug development is leading the way in exploiting molecular biological and genetic information to develop "personalized" medicine. The new paradigm is to develop agents that target the precise molecular pathology driving the progression of individual cancers. Drug developers have benefited from decades of academic cancer research and from investment in genomics, genetics and automation; their success is exemplified by high-profile drugs such as Herceptin (trastuzumab), Gleevec (imatinib), Tarceva (erlotinib) and Avastin (bevacizumab). However, only 5% of cancer drugs entering clinical trials reach marketing approval. Cancer remains a high unmet medical need, and many potential cancer targets remain undrugged. In this review we assess the status of the discovery and development of small-molecule cancer therapeutics. We show how chemical biology approaches offer techniques for interconnecting elements of the traditional linear progression from gene to drug, thereby providing a basis for increasing speed and success in cancer drug discovery.     

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.     

Conscientious Objection in Healthcare Provision: A New Dimension

The right to conscientious objection in the provision of healthcare is the subject of a lengthy, heated and controversial debate. Recently, a new dimension was added to this debate by the US Supreme Court's decision in Burwell vs. Hobby Lobby et al. which effectively granted rights to freedom of conscience to private, for‐profit corporations. In light of this paradigm shift, we examine one of the most contentious points within this debate, the impact of granting conscience exemptions to healthcare providers on the ability of women to enjoy their rights to reproductive autonomy. We argue that the exemptions demanded by objecting healthcare providers cannot be justified on the liberal, pluralist grounds on which they are based, and impose unjustifiable costs on both individual persons, and society as a whole. In doing so, we draw attention to a worrying trend in healthcare policy in Europe and the United States to undermine women's rights to reproductive autonomy by prioritizing the rights of ideologically motivated service providers to an unjustifiably broad form of freedom of conscience.     

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.     

The economics of personalized medicine: commercialization as a driver of return on investment

Optimizing commercialization of drugs is the sine qua non of the pharmaceutical industry and intensive work has been done to characterize fully the drivers of drug adoption and understand the resources required to optimize those drivers for full adoption of drugs. Conversely, while the pharmaceutical industry is actively embracing the new personalized medicine (PM) paradigm, much work remains to be done to understand fully what drives adoption of targeted therapies and how to resource those drivers appropriately. While the industry is slowly learning from its early missteps, progress is still inhibited by a lack of understanding of the specific hurdles that individual development teams face in developing and commercializing targeted therapies and the requirement for budgets specifically aimed at driving test adoption. This article considers the benefits of optimizing commercial planning in the PM space and the potential negative impact in potentially failing to optimize that planning. Real world insights are used to illustrate that a far broader commercial lens is required in the PM space and will touch on functional areas not usually included in the context of 'commercial' decisions.