P4 Medicine describes a paradigm of care that is predictive, preventive, personalized, and participatory. Rather than treatment of illness, P4 focuses on prevention and on individual and population wellness. Ideally, disease is predicted at a cellular level before symptoms develop.
P4 departs from the gold-standard research methods of evidence-based medicine (EBM), in which blinded, randomized controlled trials use representative samples of a population to verify the effects of treatments. Care is instead tailored to the individual, and each patient acts as his/her own control in predicting response to therapy (1).
P4 is an interdisciplinary approach to medicine which aims to engage computer science, basic science researchers, and clinicians. Further, P4 relies on large-scale social participation. Consumers must be activated and engaged in wellness. They must also be willing to collect and share personal health data (1).
History and development
The twentieth-century discovery of human leukocyte antigens (HLA), and the eventual mapping of the human genome, laid the groundwork for preventive medicine and for the systems biology conceived by Dr. Leroy Hood (2). HLA studies spurred the development of "a wide variety of computational tools for data collection, storage and analysis," and they elucidated the basic immunologic variations that can lead to individual susceptibility or resistance to disease (2).
Hood later postulated that "Disease arises as a consequence of disease-perturbed networks in the diseased organ that propagate from one or a few disease-perturbed networks to many as the disease progresses" (3). Perturbations can occur through genetic mutations, environmental influences, or both. Therefore, given the massive complexity of individual biology, and the even greater complexity imposed by the individual's environment, Hood asserted that "A systems-driven, cross-disciplinary environment will be a fundamental necessity for the biology of the future" (4). He established the cross-disciplinary, independent, non-profit Institute for Systems Biology (ISB) in Seattle in 2000 (4, 5).
Major work by ISB researchers included a mouse model of prion-induced neurodegenerative disease, in which RNA sequences from infected mice were compared to those of healthy mice throughout the disease course. Researchers were then able to identify differentially expressed genes in the brains of diseased mice (4).
Implications for informatics
P4 medicine relies heavily on massive computing capacity. Diverse, multivariate data sets must be collected, organized, synthesized, and analyzed. Data must be made available to researchers, and useful, actionable information must be made available to clinicians and the wider public. Consequently, many informatics implications of P4 medicine can be identified:
- P4 requires a new taxonomy of disease, based on pathogenesis rather than clusters of symptoms (1).
- People must also be classified into subgroups, based on their reactions to drugs and risk for disease (1).
- A “personal data cloud" must be generated for each individual, "containing all of the multidimensional health data for each individual collected over time -- including one's genome, blood measurements, lifestyle data (activity levels and stress, among others), transcriptome and gut microbiome data" (1). Clearly, this personal data cloud will require extensive technical, legal, and political privacy protections.
- Developers and proponents of P4 call for "well characterized and openly shared biological resources and data repositories" (2). Technical capacity as well as human expertise will be needed to gather, curate, and content for these repositories.
- Data to support P4 will be sourced not only from clinical encounters but also lifestyle information, ideally collected and submitted by patients themselves using smartphones and other mobile devices (1).
- Consumers must be empowered with access to information. As Hood and colleagues state, "...the driver of an emerging P4 healthcare system will be information consumers can use to better manage their health" (1). Consumers must also be educated to effectively understand the information available.
- Given the massive data sets needed to implement P4, CIS systems should assist users in separating signal from noise (1).
- Information must be integrated "from the different levels" of genetic information, cells, organs, individuals, populations, and environments (3). P4 proponents suggest that ”A new information commons will emerge as a digital infrastructure is even now being created by a multitude of efforts to mine these data by connecting widely disparate data sources" (1).
1. Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Personalized Medicine. 2013;10(6):565-576.
2. Auffray C, Charron D, Hood L. Predictive, preventive, personalized and participatory medicine: back to the future. Genome Medicine. 2010;2(57).
3. Hood L, Balling R, Auffray C. Revolutionizing medicine in the 21st century through systems approaches. Biotechnol J. 2012;7:992-1001.
4. Hood L. Systems biology and P4 medicine: past, present, and future. Rambam Maimonides Med J. 2013 Apr;4(2);1-15.
5. Institute for Systems Biology [Internet]. Seattle, WA: Institute for Systems Biology; 2016. Available from: https://www.systemsbiology.org.
Submitted by Carrie Grinstead