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European Biopharmaceutical Review

Innovation in Personalised Medicine


The flood of data offered by genetic profiling, population databanks and the rise of omics has yet to translate into a revolution in personalised medicine. Open innovation and collaboration could hold the key to realising their potential. Unlocked Potential In an article in Nature, Dr George Poste wrote, “proteomics and DNA microarrays have contributed a voluminous literature of more than 150,000 papers documenting thousands of claimed biomarkers, but fewer than 100 have been validated for routine clinical practice”(1). Poste pinpoints a fundamental struggle in the progress of biomarkers and their contribution to personalised medicine.

We live in an era of data-generating technologies, including full-genome sequencing, which provides raw data on an individual's DNA. Data are abundant, but neither genome-wide analyses nor the thousands of biomarkers have fully realised their expected significance within diagnostics or therapeutics. Today’s conundrum is how to turn data into information that demonstrates proof of concept, and then to turn that information into meaningful knowledge that is useful in a practical sense.

Data Overload

Genetic data alone does not provide complete answers. Since Garrod inferred the existence of inherited biochemical characteristics from family histories of alkaptonuria at the turn of the last century, it has been increasingly accepted that the aetiology of most common diseases involves not only discrete genetic and environmental causes, but also interactions between the two (2,3). No single data source stands alone as the single key to realising personalised medicine. The massive data that result from association of multiple variables will be staggering, but, ultimately, understanding geneenvironment interactions will provide valuable insight.

We might be experiencing ‘information overload’, the term Alvin Toffler popularised in his book Future Shock, penned in 1970 (4). Toffler wrote "when the individual is plunged into a fast and irregularly changing situation, or a novelty-loaded context [...] his predictive accuracy plummets. He can no longer make the reasonably correct assessments on which rational behaviour is dependent". Is it simply the vast amount of data holding us back? Poste touches upon a central issue – how to turn data into meaningful knowledge. Technological advances can provide answers; however, it is up to us to ask the right questions.

The proverbial saying "you can’t see the wood for the trees”, is an apt analogy for the current state of personalised medicine. The scientific community has the resources, the methodology, the technology, and the meticulously collected biomaterials and corresponding databases. The result is an abundance of peer-reviewed scientific biomarker articles, but few novel compounds for clinical use and a handful of diagnostic methods. To explain why, inspiration can be drawn from history and the great scientists that made scientific paradigms that continue to be used to this day.

Historical Perspective

In the 17th century Sir Francis Bacon shaped our empirical methods. Thereafter, great scientists combined observation and biological samples, which resulted in the scientific backbone of taxonomy, genetics and evolution. Legendary scientific achievements by Linnaeus, Mendel and Darwin systemised nature and established our paradigms of science because they found the right touchstones: gender in the botanic world of taxonomy; genes in the field of inheritance; and adaptation in the world of evolution.

During the 20th century, landmark scientific research unlocked the genetic code, and technological developments brought DNA sequencing and computational process. In combination, we are able to elucidate complex diseases where several factors influence progression and outcome. However, we might be placing too strong an emphasis on genetic data, and underplaying information related to adaptation, environment and phenotypes.

Looking back to recent decades, even evidence-based medicine – medical decision-making based on empirical data relying on the law of averages – was not initially accepted. It should not be surprising that a great hurdle to overcome remains before the implementation of personalised medicine, which enables a group of patients with one disease to be treated with different medicine based on individual variances, becomes accepted.

Taking another perspective, medical matters continue to extend their grasp over our existence. According to World Bank figures, public expenditure on healthcare in the EU could jump from eight per cent of GDP in 2000 to 14 per cent in 2030 and continue to grow beyond that date. The Economist Intelligence Unit cites the main drivers: the ageing populations and the related rise in chronic disease; costly technological advances; patient demand driven by increased knowledge of options and by less healthy lifestyles; and legacy priorities and financing structures that are ill-suited to today’s requirements (5).

Ivan Illich, in Medical Nemesis: The Expropriation of Health, defined clinical iatrogenesis as direct harm to the patient caused by medical intervention (6). The scope of iatrogenesis goes far beyond overt error; pharmaceutical interventions almost inevitably have a risk of unintended, adverse consequences. Illich went on to define social iatrogenesis as the medicalisation of life. Medicine extends its domain over more and more of our existence, as economic terms support. Does the delayed advent of personalised medicine contribute to medicine’s economic impact?

Illich attributed part of the medicalisation of life to ‘the Sisyphus Syndrome’ – modern health care may keep people alive, but with chronic diseases requiring yet more health care (7). Availability generates demand. Scientists continue to define new diseases and pharmaceutical companies continue to develop and advertise their putative cures, which may, in fact, as we know from the tirade of advertisement disclaimers, affect the drug consumer in ways that require yet more intervention.

Population Biobanks

Choosing the right approach in personalised medicine, and especially for complex, multi-faceted diseases like cancer and dementia, remains a great challenge. While we can continue to collect single-nucleotide polymorphisms (SNPs) and DNA sequences perhaps the keys to unravelling and organising the data might be found in a combination of other sources (for example, epigenetics, environment, nutrition, physical activity and medication). Longitudinal population biobanks with biomaterial libraries that are richly annotated by thousands of variables, in addition to genetic characterisation and the other omics, could be an excellent starting point.

In general, the biobanking market is in a growth phase as scientific researchers migrate towards the use of better and more relevant tools, such as human tissues and biomaterials, for primary research, pharmaceutical and diagnostic-tool development. Population biobanks, which are a specialised niche, are large repositories of donated human biomaterials, collected from volunteers with and without disease. These samples and data can be used to identify a wide variety of contributions to human disease including demography, phenotype, nutrition, environment, medication and family history. Population biobanks are part of the Scandinavian tradition, with centralised collection of health data linked by unique personal identification numbers assigned to every citizen at birth.

For example, over the last 25 years the Norwegian University of Science and Technology has repeatedly collected biomaterial samples and associated phenotypic, lifestyle, genetic, clinical and environmental data from the general population of Norway’s Nord Trøndelag region, and as of today includes over 2 million biological-sample aliquots and up to 6,000 phenotypic and environmental variables from about 100,000 individuals. Population biobanks are unique data sources for longitudinal studies and allow access to samples drawn prior to disease onset, making early-disease prognastic biomarker identification and validation possible. For example, MRI images have been collected in combination with biomaterial and rich annotation that can be valuable in diagnostics and treatment for slowly progressive dementia.

These resources have the potential to play a pivotal role in the realisation of personalised medicine as a stronger emphasis is placed on combining non-genetic and genetic variables, especially in the formulation of reverse, longitudinal models based on clinical end-points.

Open Innovation: The Way Forward

The advantages of multi-faceted data available from a population database are obvious. Forward-thinking companies have recognised this and use the data to develop diagnostic tools and treatment for several diseases, including cancers, dementia, diabetes, arthritis and others.

So the question remains: what will it take for the industry as a whole to make personalised medicine a reality? A recent McKinsey report that stated “the good old days of the pharmaceutical industry are gone forever. Even an improved global economic climate is unlikely to halt efforts by the developed world’s governments to contain spending on drugs. Emerging markets will follow their lead and pursue further spending control measures. Regulatory requirements – particularly the linkage among the benefits, risks and cost of products – will increase, while the industry pipeline shows little sign of delivering sufficient innovation to compensate for such pressures” (8).

One merely has to read the news to realise that these are turbulent times for the pharmaceutical industry. Companies need profit to invest in new products: will Big Pharma help identify the keys to making personalised medicine a reality if the results do not provide a competitive advantage, but rather can be used by all industry players? Will personalised medicine require a major shift in the pharmaceutical industry paradigm?

Inspiration can be found in Dr Henry Chesbrough’s theory of ‘open innovation’ – a trend to form innovation partnerships involving companies, universities, government institutes and others, that is a way of managing R&D to make ideas and collaborations flow more freely, and innovation proceed more quickly. Communities of interested participants work together across boundaries in an iterative interactive process. The breadth, depth and quality of these communities can become a competitive advantage.

In the Ivey Business Journal Online, Chesbrough wrote that “innovation is constantly changing, as is the process by which new ideas and technologies get to market […] The best approach is to embrace the idea that innovation will continue to change, and that organisations that seek to profit from innovation must take on the challenge of changing with it” (9).

The path ahead for personalised medicine is not straightforward. Change will not happen overnight, yet there are glimmers of progress. As stated earlier, we have the resources, the methodology, the technology and the meticulously-collected biomaterials and corresponding databases. But we do not necessarily hold the keys to unlocking the potential. First we need to address fundamental weaknesses in today’s technological ‘religion’. Technology is not the solution itself; technology should be used to find solutions that become practical knowledge to benefit future society.


  1. Poste G, Bring on the biomarkers, Nature 469: pp156-157, 13th January 2011
  2. Garrod A, The incidence of alkaptonuria: a study in chemical individuality, Lancet 2: pp1,616-1,620, 1902
  3. Hunter DJ, Gene-environment interactions in human disease, Nature Reviews Genetics 6: pp287-298, 2005 4. Toffler A, Future Shock, 1970
  4. The future of healthcare in Europe, The Economist Intelligence Unit Limited, 2011
  5. Laws M, Medical Nemesis [Web log comment], Retrieved from http://healthvsmedicine.blogspot. com/2005/02/medical-nemesis.html
  6. Illich I, Medical Nemesis: The Expropriation of Health, 2000
  7. Hunt V, Manson N and Morgan P, A wake-up call for Big Pharma, McKinsey Quarterly, December 2011
  8. 9. Chesbrough H, Management innovations for the future of innovation, Ivey Business Journal Online, Innovation, May/June 2011

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Håkon Haaheim is Chief Business Officer of HUNT Biosciences AS. A medical microbiologist and molecular diagnostics specialist, Håkon holds an MBA in strategy and management. He has worked for the University Hospital of North Norway, as Innovation, Health Innovation Coordinator, and a member of the Norwegian Health Innovation Coordination Team. During part of this time he also held an adjunct BDO position for the Technology Transfer Office for the North Norway region. Håkon has been engaged with several start-up companies including Fastmed Innovation, GenoVision (merged with Qiagen GmbH in 2004), Prophylix Pharma, and ProCelo AS, where he holds key patents and is appointed Chairman. He joined HUNT Biosciences AS in September 2009.
Håkon Haaheim
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