January 1, 1970 (Vol. , No. )

From the previous chapter’s discussion of turning biomarkers into companion diagnostics, we now shift to a discussion of healthcare. Personalised medicine has an impact on the healthcare budgets. Are the tools that healthcare economists and pricing and reimbursement authorities currently use suitable for analysing personalised medicine?

In short, the concept of personalised medicine revolves around the idea of the selection of patients who are likely to respond the medicine, so that the treatment will offer better outcomes. According to the recent Quintiles report “biopharma and managed care executives are optimistic that personalised medicine will improve efficacy, safety and public health”.1

However, there is a widespread scepticism about the financial impact of personalised medicine. According to the same report 56% of managed care executives feel that personalised medicine will increase cost of prescription medicines. Consequently, pricing and reimbursement of personalised medicines need careful consideration and a balanced view on cost-effectiveness and incentives for innovation.

There is a widespread skepticism about the financial impact of personalized medicine. [AlienForce – Fotolia.com]

Another Economic Model is Needed

Much of our experience with economic evaluations of medicines is based on medicines designed to treat the whole patient population. Evaluations of the economic value of a particular drug usually involve comparisons with treatment alternatives or palliative care. Such comparative effectiveness are typically assessed on the basis of data collected in randomised controlled trials across a broad population of patients.

There have been attempts to identify sub-sets of patients that benefit most noticeably from the medicine, i.e. patients over or under a certain age or patients judged to have a ‘severe’ form of a certain disease. But in many cases these evaluations were not supported by robust evidence, and the sub-groups were not well characterised nor were they statistically well-sampled in the clinical data.

Testing May Be Economically Viable

In the personalised medicine concept, a sub-group of responders is selected or screened out, which raises the hope that economic evaluations will be more straightforward and positive. This is particularly true for the cheapest use of costly molecular targeted agents.2 Biomarkers can improve the ability to identify responders and non-responders, and insure that the information is used to make better treatment choices. As a consequence, the respective drug gains a more favourable risk-benefit ratio, patients are expected to have better health outcomes and better quality of life and healthcare resources are likely to be used more efficiently.3

Much of the efficiency gains of personalised medicines depends on the testing—in other words, “testing before treating may be economically viable if the savings gained by avoiding ineffective treatment and adverse events are greater than the cost of testing.”4

Studies on the cost-effectiveness of personalised medicines are still on the way but promising results are available. The introduction of a companion diagnostic strategy in advanced non-small cell lung cancer reduced overall treatment costs by more than € 800 compared to current treatment.5 A cost-effectiveness analysis in the field of chronic myeloid leukaemia showed that the use of the FISH test reduced treatment costs by €12’500.6 In addition, it increased the life quality.

Reduction of R&D Costs

Personalised medicine may provide more value for money—not only because of improved drug effectiveness and reduced toxicity, but it may also decrease the average research and development costs for new medicines.

In fact, clinical trials are the most expensive part of R&D (nearly 50% of the investment). The costs of clinical trials seem to have risen by one third between 2005 and 2007 due to increasing regulatory and other requirements.7 Biomarkers may enhance the efficacy of clinical trials of new drugs by investing more heavily in early research to identify key biomarkers, and in targeting relevant sub-groups of patients. Smaller (and maybe even shorter) clinical trials are likely to reduce development costs.8

This sounds promising, but since personalised medicine is in its early stages, efficiency gains may occur only in the long run.9 Furthermore, companion diagnostics are likely to increase the additional costs and the complexity of the risky process of drug discovery and development. In oncology, where personalised medicine is currently progressing most rapidly, the late stage failure rate of new compounds has historically been higher than in any other area.

A well-known argument against personalised medicines is, that they will lead to smaller groups of eligible patients and therefore lead to higher unit prices in order to deliver competitive return on investment. Additional value, therefore, “needs to be captured in terms of premium pricing, faster adoption or longer effective patent life for a portfolio of targeted drugs, to offset the reduction in potential revenues from market stratification”.10 However, personalised medicines do not only diminish groups of eligible patients, they enlarge them as well (see chapter 2).

The following problems still have to be solved:

  • The costs of diagnostics are not easy to describe
  • The evaluation of cost-effectiveness is difficult
  • The regulatory pathway is fragmented

Costs of Diagnostics Are Not Easy to Describe

Implementing personalised medicines in healthcare is potentially a costly investment: it requires testing a whole patient population to identify groups of responding patients or to screen out patients likely to suffer adverse events or who need different dosing.

Some evaluations have attempted to tie the diagnostic to the use of a specific new medicine (“co-dependent technologies”) and have in some cases passed the costs of diagnosis on to the medicines developer. But more and more multiple tests and multiple personalised medicines for particular diseases become available. For example, in colorectal cancer and non-small cell lung cancer, a set of parallel tests are to be performed on a number of molecular biomarkers to decide between a range of personalised medicines. But more complex diagnostic and treatment pathways make it less easy to ascribe costs of diagnosis to the use of a particular medicine.

Evaluation of Cost-Effectiveness is Difficult

To evaluate the cost-effectiveness of particular molecular diagnostic approaches is also problematic. Diagnostics are normally supported by analytical performance data and rarely by clinical or outcome data, but in order to perform a health technology assessment you need the latter. According to a systematic review, only eight studies evaluated the clinical validity, and none of the studies was a prospective evaluation of a test’s clinical utility.11

In addition, personalised medicines may also require a different view on the clinical evidence available when a medicine is launched. In some examples, personalised medicines have been approved on the basis of a retrospective analysis of clinical data identifying the responsive sub-population. In other cases, personalised medicines have been launched under conditional approval mechanisms on the basis of phase 2 data alone (smaller studies, often without overall survival end points).

Regulatory Pathway is Fragmented

Eventually, market access of personalised medicines is highly dependent on the assessment process, in particular health technology assessment and pricing and reimbursement decisions. It is very costly to produce better evidence on the clinical utility of genomic tests for cancer prior to obtaining reimbursement. The current EU regulatory pathway for development and approval of drugs and companion diagnostics is fragmented. Ramsey et al. propose more creative funding strategies such as coverage with evidence development. However, “such an integrated model will require that test manufacturers, clinical trials groups and insurers modify their current ways of operating and paying for trials and treatment.”12


Current health economic research shows that personalization of treatment, i.e. identifying responders and non-responders, has the potential to improve effectiveness and reduce costs. In addition, biomarker testing may lead to more successful R&D projects. However, the value of so-called tailor-made therapies depends to a large extent on the quality of the tailor. In other words, adaptations to both regulatory structures and market structures are necessary to encourage the development of personalised medicine.

Click here for the next and last chapter in this series, where you’ll learn about personalized medicine’s potential impact on society.


Alexander Roediger is director, European Union affairs at MSD (Europe).


This article first appeared as chapter 4 of a white paper published by the EuropaBio Personalised Medicine Taskforce entitled “Personalised Medicine: Status Quo and Challenges”.

EuropaBio represents 56 corporate members and 14 associate members and BIO regions, and 19 national biotechnology associations who in turn represent some 1800 small and medium sized biotech companies in Europe. Members of EuropaBio are involved in research, development, testing, manufacturing and commercialisation of biotechnology products and processes. Our corporate members have a wide range of activities: human and animal health care, diagnostics, bio-informatics, chemicals, crop protection, agriculture, food and environmental products and services.

1 Quintiles (2011), The New Health Report. 2011. Exploring Perceptions of Value and Collaborative Relationships Among Biopharmaceutical Stakeholders, p. 18; see: www.quintiles.com/newhealthreport
2 Soria JC, Blay JY, Spano JP, Pivot X, Coscas Y, Khayat D (2011), Added value of molecular targeted agents in oncology; Annals of Oncology, p. 1-14
3 Sadée W, Zunyan D (2005), Pharmacogenetics/genomics and personalised medicine; in: Human Molecular Genetics, Vol. 14, Review Issue 2: 207-214; p. 207
4 Gaultney JG, Sanhueza E, Janssen JJ, Redekop WK, Uyl de Groot CA (2011), Application of cost-effectiveness analysis to demonstrate the potential value of companion diagnostics in chronic myeloid leukemia; Pharmacogenomics 12(3), 411-421
5 Zaim R, Gaultney JG, Redekop WK, Uyl-de Groot CA (2011), Potential benefits of introducing a companion diagnostic in advanced non-small cell lung cancer; ISPOR 14th Annual European Congress, Madrid
6 Gaultney JG, Sanhueza E, Janssen JJ, Redekop WK, Uyl de Groot CA (2011), Application of cost-effectiveness analysis to demonstrate the potential value of companion diagnostics in chronic myeloid leukemia; Pharmacogenomics 12(3), 411-421
7 See Rawlins M (2008), De testimonio: on the evidence for decisions about the use of therapeutic interventions; in: Lancet 372:2152-61, p. 2156; Collier R (2009), Rapidly rising clinical trial costs worry researchers; in: CMAJ 180(3):277-278
8 Deverka PA, Vernon J, McLeod HL (2010), Economic Opportunities and Challenges for Pharmacogenomics; in: Annual Revue of Pharmacology and Toxicology 50:423-37; p. 427
9 Lewensohn D (2010), Towards Personalised Healthcare (Karolinska Institutet, in association with Science Business), p. 7f.
10 Deverka PA, Vernon J, McLeod HL (2010), Economic Opportunities and Challenges for Pharmacogenomics; in: Annual Revue of Pharmacology and Toxicology 50:423-37, p. 429
11 Ramsey SD, Veenstra D, Tunis SR, Garrison L, Crowley JJ, Bakerh LH (2011), How Comparative Effectiveness Research Can Help Advance ‘Personalised Medicine’ In Cancer Treatment; Health Affairs, 30, no. 12:2259-2268
12 Ramsey SD, Veenstra D, Tunis SR, Garrison L, Crowley JJ, Bakerh LH (2011), How Comparative Effectiveness Research Can Help Advance ‘Personalised Medicine’ In Cancer Treatment; Health Affairs, 30, no. 12:2259-2268

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