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Personalised medicine

Our future approach to health, wellness, and medical needs will be very different.  Rather than treatments statistically averaged to the general population, in future we will tailor medicine to each individual’s specific genome and biochemistry, with potential treatments and drug protocols tested first on sophisticated medical ‘avatar’  - a detailed model of the patient’s personal physiology.

People will be able to manage their health with highly customized lifestyle plans – diet, exercise, relaxation – as well as manage acute (sudden onset) health problems with precision.  Genomics and genetic analysis will forecast potential problems that may be treated before they arise.  When required, medical interventions will create fewer side effects, as drugs will be targeted and genomic and metabolomic interactions between drugs and patients will be modelled and addressed in advance of treatment.  Many degenerative diseases will be conquered – but new diseases may arise from emerging patterns of social and technological stress.

What are the major trends in science and technology likely to continue by 2030? 
Personalized medicine or PM is a medical model that proposes the customization of healthcare - with medical decisions, practices, and/or products being tailored to the individual patient. It denotes the use of some kind of technology or discovery enabling a level of personalization not previously feasible or practical.

A smart combination of ICT-enhanced monitoring, big data and applied genetic information would enable a really individualized approach of patients or – more in general – people and their health issues.

This could yield huge efficiencies and benefits, especially in regions with an ageing population (see the future ' Super Centenarian Society: more people suffering from combinations of chronic diseases). PM would make medication more effective, while substantially reducing negative side effects from conflicting medication.

Personalized medicine would broaden the scope of current health care practices, with much more emphasis on prevention and early detection of diseases. By taking as a point for departure of (preventive or curative) treatment not the symptoms or mechanisms of disease or complaints of the patients, but a digital  "avatar", containing all genetic information, many ailments (or the individual's susceptibility to it, can be early diagnosed and treated, or be controlled, e.g. by reconsidering life-style choices, diets (nutritional immunology approach) et cetera.

  • With 3-D printing customized polypills or tablets can be produced, enabling "individualized dosing and automated fabrication of medicines containing multiple drugs," in addition to custom single-drug products).
  • With companion diagnostics, molecular assays measure levels of proteins, genes, or specific mutations to provide a specific therapy for an individual's condition - by stratifying disease status. These and related technologies could help assesss patient's risk factor for a numer of conditions, selecting the proper medication, and tailoring dosages to that patient's specific needs.
  • Pharmacogenetics (also termed pharmacogenomics) uses a personal digital 'genetic mirror', in combination with a database on the impact of genetic variation and drug responses, specified  by biomarker (medicine), to establish optimum medication and dosage at individual level. Digitised ' bodies' , genetic mirrors and biobanks speed up the development of new types of drugs and therapeutics considerably, as most of the clinical trials trajectory can be done in virtual reality.
  • Proteomics focusses on the analysis of all of the proteins and protein isoforms, that eventually control biological functions like growth, death, cellular movement and localization, differentiation, etc. Many diseases such as cancer are functionally manifest as dysfunctional protein signal transduction. With pharmaceutical interventions ' wrong' protein activity can be modulated.
  • The effectiveness of oncology (from prognosis to treatment suggestions) is enhanced as new methods enable to test for global gene, protein, and protein pathway activation expression profiles and mutations in cancer cells from patients. With cancer genetics hereditary cancer risk can be established also for individuals without a strong family history.

What are the early signals likely to emerge in personalized medicine and the major changes/disruptions they can generate?

  • Massive dataprojects like the human genome and the virtual brain project lay the foundation for much PM research.
  • Personalized '"health scans" and telemonitoring practices generating Big data (e.g. 24 hour hart monitoring) provide the first steps towards creating the ' personal health cloud / avatar / mirror etc.
  • An approach seeking to establish hereditary susceptibility for certain diseases (especially cancers) is gaining field. The preventive amputation of Angelina Jolie's breasts is an extreme example of its consequences.
  • US has introduced a Genomics and Personalized Medicine Act in Congress to address scientific barriers, adverse market pressures, and regulatory obstacles.
  • Insurance companies and employers might discriminate people based on their health care risks profiles.

What sort of policy response is needed to create sciences-and-technology-innovation-friendly "ecosystems" around personalized medicine?
The required ecosystem for PM would be a integer data environment and well developed monitoring and measuring technologies that are needed to generate the big health data at individual level as a basis for PM diagnostics and therapeutics.

What sort of policy responses are necessary to make emergent technologies fitting with broader policy objectives and citizens' expectations?
- A clear and mature legal framework, complemented by regulatory and enforcement practice has to be developed, or an innovative approach to the same effect to guarantee the integrity, confidentiality and quality of personal health data profiles and the exchanges around it. Information transparency is key. Who owns the data (the individual [trust] or the authority [protected data-monopoly]?

- Even if risk-assesment is sorted out, the impact of the choices made based on the outcome of these assessments are often not really well known. More sound research in those areas is needed and the many ' unknowns'  should be detected and explored.

- Personalised Medicine is a disruption of the current practice where a medical specialist provides certainty and the therapy or cure needed. It is a shift in the direction of risk-assesment by non-medical specialists, where people will have to make high-impact choices regarding their life-style, medical intervention and others. First issue at stake here: the scientific soundness of the risk-assesment.

- PM is a game changer: the positions of big pharma, health specialists and citizens / consumers / patients will shift. Governement needs to reassess its own role in this field. How to empower citizens, how to tackle their inability to deal with this sensitive information and the complex choices around it? How to prevent that industry backs away from developing medication?

- Health insurance will have difficulty dealing with PM as long as it is based on treatment provided, rather than treatment prevented or health gained.


“GnuBio launches as an open-source genome sequencing startup,” Julie M. Donnelly, Mass High Tech, June 3, 2010, at .

“Carrots, Sticks and Digital Health Records,” Steve Lohr, New York Times, February 26, 2011, at .

“Virtual Reality Avatars as Health Advocates,” by Kendra Wyatt, Changemakers, at

  • New possible monopoly-like positions for institutions with regards to personal / individual (big) health data should be controlled.
  • Genetic testing can induce discrimination from insurer or employer.
  • Investments in certain developments might not take place because of development risks. To develop a companion diagnostic would generate less income for industry that conventional medicine.
  • Reimbursement issues: who pays the upfront costs for creation of diagnostic basis (personal data profiles etc.) and preventive therapeutics based on those, thus not harvesting predictive potential and preventive potential (resulting in high ' avoided costs'), to save treatment costs later on?
  • Insurance issues: insurance premiums today are based on actuarial statistics that apply to large, predictable populations. By contrast, personalized medicine targets small populations, which are far less stable and predictable from an actuarial standpoint. New actuarial assumptions are needed on which to base reimbursement models. A trend towards pay for performance instead of pay for treatment could help the adoption of personalized medicine.
  • Primary care providers may have to build new service lines around prevention and wellness in order to replace revenues lost from traditional medical procedures. Physicians will also require a solid background in genomics and proteomics to make the best use of new data.
  • A more efficient drug development process, based genetic risk factors.
  • Marketing of therapies on the basis of a companion theranostic test.
    PM will increase the efficacy and safety of medications, while reducing time, cost, and failure rates of clinical trials in the production of new drugs by using precise biomarkers.
  • Gene-centered research will speed the development of novel therapeutics.
    With each person having a virtual counterpart, medication can be tested and optimized, resulting in lower cost and higher effectiveness of disease treatment. A challenge would be the increase of already existing problems around integrity, trustworthiness and confidentiality of personalized health data.
    For healthcare providers, personalized medicine offers the potential to improve the quality of care through more precise diagnostics, better therapies, and access to more accurate and up-to-date patient data.
Who owns the health data (the individual [trust] or the authority [protected data-monopoly]?
How to empower citizens to deal with sensitive health information and the complex choices around it?
Who will cover costs for developing medicines / treatments tailored to 9 bn + people?
How will individualised results of treatments affect medical research and testing?



Underpinning policy ideas