Background Paper 7.4 Pharmacogenetics and Stratified Medicine › medicines › areas ›...

Priority Medicines for Europe and the World

"A Public Health Approach to Innovation"

Update on 2004 Background Paper

Background Paper 7.4

Pharmacogenetics and Stratified Medicine

By Susanne J.H. Vijverberg, Anke-Hilse Maitland-van der Zee

March 30, 2013

Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands

This Background Paper was commissioned by the WHO Collaborating Centre for Pharmaceutical

Policy and Regulation (www.pharmaceuticalpolicy.nl). This work was financially supported by the

Dutch Ministry of Health, Welfare and Sport.

https://solismail.uu.nl/owa/redir.aspx?C=UC-4ThrLF0OVgcLLsgB07HKeOSNMIdAIF1CpEdVv9WTL0lwEC017_2qKrKavEipiPoYltgQ4e-U.&URL=http%3a%2f%2fwww.pharmaceuticalpolicy.nl

Update on 2004 Background Paper, BP 7.4 Stratified Medicines

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Table of Contents

Acknowledgements ............................................................................................................................................ 3

Glossary of Terms ............................................................................................................................................... 4

Executive Summary ............................................................................................................................................ 5

1. Introduction ................................................................................................................................................. 6

2. From personalised medicine to stratified medicine............................................................................. 8

3. From monogenic to polygenic approaches ............................................................................................ 9

4. Biomarkers and disease profiling ......................................................................................................... 10

5. Research ..................................................................................................................................................... 12

5.1 Study design ...................................................................................................................................... 12

5.2 Defining Outcomes ........................................................................................................................... 15

5.3 From candidate-gene approach to GWAS and whole-genome sequencing ............................. 15

5.4 Other -omics technologies ............................................................................................................... 17

5.5 Replication and validation of findings .......................................................................................... 18

5.6 Future diagnostics: from genotype to diagnosis .......................................................................... 19

6. What European collaborations and EU-funded initiatives exist?.................................................... 19

7. Genomics initiatives in low resource settings .................................................................................... 20

8. Challenges for implementation ............................................................................................................. 21

8.1 Pricing and reimbursement ............................................................................................................. 22

8.2 Assessing clinical value ................................................................................................................... 23

8.3 Regulation of products..................................................................................................................... 25

8.4 Legal and ethical issues ................................................................................................................... 26

8.5 The emergence of orphan populations .......................................................................................... 27

8.6 Technology infrastructure ............................................................................................................... 27

8.7 Education and training .................................................................................................................... 28

9. Identified gaps and recommendations for research and policy ...................................................... 28

References ........................................................................................................................................................... 31


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Acknowledgements

We would like to acknowledge Christine Leopold (Austrian Health Institute) for her

assistance on the pricing and reimbursement section, as well as Naho Yamazaki and Richard

Malham (The Academy of Medical Sciences, United Kingdom) for their valuable comments

and suggestions on this chapter.


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Glossary of Terms

Adverse drug reaction: an unintended, harmful reaction to medicines.

Biobank: collection of biological samples and associated information stored for research

purposes.

DNA: molecule that carries the genetic information in a cell. DNA composed of nucleotides.

DNA methylation: biochemical process in which a methyl group is added to cytosine or

adenine nucleotides.

Disease profiling: genetic or molecular profiling of disease.

Enzyme: protein involved in a specific chemical conversion.

Exome: part of the genome formed by DNA sequences that encode genes (exons).

Gene: segment of DNA coding for an inheritable characteristic.

Genetic variation: naturally occurring genetic differences between individuals.

Genome: total content of genetic information in a cell.

Genotype: genetic constitution of an individual.

Genome-Wide Association Study (GWAS): study to assess common genetic variations

across the entire genome of a large population of individuals in order to study whether any

of the investigated variations is associated with a phenotype of interest.

Linkage studies: Genetic linkage is the tendency of DNA segments to be inherited together.

A genetic linkage studies aims to identify a genetic marker that inherits with the

(unidentified) gene associated with the phenotype of interest.

Metabonomics (or metabolomics): study of the effect of a systematic change in a biological

system caused by an intervention (such as drug administration or specialized nutrition).

Monogenetic trait: phenotype caused by one single gene.

Non-coding RNA: RNA molecule that is not translated into a protein.

Pharmacoepigenomics: assessing the relationship between variation in physical DNA

structure and treatment outcome.

Pharmacogenetics: assessing the relationship between variation in a gene and treatment

outcome.

Pharmacogenomics: assessing the relationship of variation in various genes (or genome-

wide) and treatment outcome.

Phenotype: observable characteristics of an individual, e.g. disease or poor treatment

response.

Polygenetic trait: phenotype caused by multiple genes.

Proteomics: study of the proteome; the regulation and production of the proteins in a cell.

Quality adjusted life year (QALY): measure of the quality of remaining life-years. Often

used in cost-effectiveness analysis.

SNP: Single Nucleotide Polymorphism is the most common type of genetic variation, in

which a single nucleotide is altered at a certain position in the genome.

Transcriptomics: study of the transcriptome; the RNA transcripts produced by the genome

and the regulation of that process.


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Executive Summary

Stratified or personalised medicine is a rapidly developing field that will have major impact

on healthcare in the coming decades. Without applying the concept of stratified medicine, a

particular treatment is targeted to the whole patient group, without being able to predict the

treatment response in patients. Stratified medicine allows medicines to be targeted to those

patients who (best) respond to therapy and/or to be avoided in patients who are most likely

to experience side effects. Thus, stratified medicine shows great promise in the improved

and safer usage of existing medicines in high, as well as low, resource settings- In addition, it

demonstrates potential for the identification of new drug targets and the development of

new diagnostic tools. The term ‘stratified medicine’, however, might be more accurate than

the term ‘personalised medicine’ in encompassing the potential and hopes of the new -omics

era for medicine and public health, in which the main focus will be in the stratification of

patient populations on the basis of biomarkers (e.g. genetic variations and protein

expression). Scientific technologies in the field of genomics and biomarker discovery are

advancing at a rapid pace, shifting from single to complex multifactorial diseases and from

monogenic (assessing one single gene) to polygenic (assessing multiple genes at the same

time) approaches. Despite the rapid scientific advances, the implementation of stratified

medicine in health care systems remains low. This is due in part to (current) scientific

limitations, and the lack of standardization for response outcomes (i.e. adverse drug

reactions which complicate the comparability of studies), which complicates the

comparability of studies. Furthermore, successful replication is generally low, and a global or

European pharmacogenomic database with a thorough inventory of available knowledge

and biological specimens is lacking. Moreover, societal and regulatory limitations hinder

implementation; there is a need for a well-organised technology infrastructure, professional

training in the use and interpretation of testing, as well as internationally aligned ethical,

legal and regulatory frameworks. A summary of research and policy priorities for stratified

medicine is given in Box 7.4.1. The clinical implementation of stratified medicine also

requires basic, translational, as well as regulatory studies.

Box 7.4.1: Key Summary of Research and Policy Priorities for Stratified Medicine

Establishing a funded European research network

Safer use of existing medicines is underused

The effect of non-genomic factors influencing treatment response should not be

underestimated

Need for a European catalogue of pharmacogenomic datasets and harmonization

program

Current regulatory guidelines and reimbursement procedures hamper

implementation

Stratified medicine in low resource settings is rare

Stratified medicine in vulnerable groups should be stimulated

Clinical added value should be assessed

Barriers to implementation should be diminished

Ethical, legal and social implications of stratified medicine should be further

investigated


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1. Introduction

Stratified medicine is a rapidly developing field that is likely to have important clinical

consequences in the coming decades. It holds strong promise for the improvement of both

the drug efficiency and the drug safety of existing drugs due to the identification of drug

responders and non-responders based on biological markers (see Box 7.4.2: the abacavir

case). Furthermore, as a result of increasing knowledge about complex multifactorial

diseases, the identification of new drug targets and the development of new diagnostic tools

will also be possible.

Genomics and other –omic technologies are rapidly evolving and are key elements in the

future of stratified medicine. Historically, human diseases have been treated on a ‘one-size-

fits-all’ basis where one drug suits all patients. The choice of a drug has been guided by

evidence-based information, professional guidelines and a ‘trial-and-error’ approach. When

a patient does not respond adequately with a prescribed drug or showed substantial adverse

drug reactions, the dosage can be adjusted or the medicine itself might be replaced by

another. However, with the unravelling of the molecular pathways underlying disease

processes and the rapid emergence of new -omic technologies (including epigenomics,

proteomics and metabonomics), research is rapidly producing new insights that will open

the door to more tailored forms of drug selection, drug dosing, drug development and

diagnostics. In this chapter we will focus mainly on the role of pharmacogenomics in

stratified medicine, as this particular field has been most successfully translated into clinical

practice in comparison to the other -omics fields. Pharmacogenomics study the influence of

genomic variation on treatment response. Two successful pharmacogenomics examples

include HLA-B*5701 genotyping and the risk of hypersensitivity to the antiretroviral

treatment abacavir and HER2 testing in breast tumour biopsies and clinical response to the

antineoplastic agent trastuzumab.

Based on individual or disease-specific biomarkers, the drug choice can be tailored in order

to improve efficacy and to minimize the occurrence of adverse drug reactions. In addition to

tailored treatment, -omics advances also hold promise for new approaches to drug

development and a better use of existing drugs, as well as the prevention of disease by the

identification of individuals at risk. Nevertheless, despite the predictions that the era of

stratified medicine has arrived, clinical implementation has been limited.1 In this paper we

will address the evolution of the field, its current level of development, as well as the

challenges and opportunities for future research and for implementation in clinical practice.


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Box 7.4.2: The abacavir case; uptake of genetic HLA-B*5701 testing

In 1998, abacavir, an anti-retroviral treatment for HIV was approved by the FDA and in 1999

by the EMEA (now EMA). The drug was generally well tolerated; however, it caused a drug

hypersensitivity reaction in a small group of patients (5 to 8%) that could be life-threatening.

From 2001 onwards there was increasing evidence of a relationship between a genetic

variation in the HLA-B*5701 gene (a gene that codes for a protein involved in immunity) and

the risk of abacavir hypersensitivity. Sales of abacavir containing drugs subsequently

declined. A genetic test to assess the HLA-B*5701 was introduced in 2005, but test utilization

remained low due to concerns about differences in HLA-B*5701 prevalence between

individuals of different populations and residual risks upon negative genetic results (See

Figure 7.4.1 for test uptake in the United States). A shift took place in 2007 upon the

completion of two industry-sponsored studies that showed the generalizability of the genetic

test across diverse geographical patient populations and patients with different genetic

ancestry and a very high negative predictive value. This together with the development of a

skin patch test to immunologically confirm the genetic test led to the rapid adoption of HLA-

B*5701 testing by HIV practitioners and the incorporation of the test in clinical guidelines. It

was only in July 2008 that the FDA included HLA-B*5701 testing recommendations on the

drug label of abacavir.2, 3 Genetic testing of HLA-B*5701 kept abacavir on the market because

it is now possible to target the drug to a patient population with almost no risk in developing

the severe hypersensitivity reaction.

Figure 7.4.1: uptake of genetic testing of HLA-B*5701 in the US

Source: Lai-Goldman M, Faruki H. Abacavir hypersensitivity: a model system for pharmacogenetic

test adoption. Genet Med 2008; 10(12):874-8.3


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2. From personalised medicine to stratified medicine

The term ‘personalised medicine’ is increasingly being replaced by the term ‘stratified

medicine’.4 According to the European Commission workshop report ‘Stratification

biomarkers in personalised medicine’ from 2010, personalised medicine may be defined as ‘a

medical model using molecular profiling technologies for tailoring the right therapeutic strategy for

the right person at the right time, and determine the predisposition to disease at the population level

and to deliver timely and stratified prevention’.5 Genomic and non-genomic biomarkers will

enable us to increasingly target treatment specifically to subpopulations of patients who are

more likely to respond to a particular treatment and are less likely to develop adverse drug

reactions, and will lead to ‘subpopulation-unique’ instead of to ‘individual-unique’ drug

targeting and development (Figure 7.4.2). In this way, the benefit-risk profile of the medicine

can be assessed per population stratum, and unnecessary (in case of non-response) or

harmful (in case of toxic effects) use of medicines may be prevented. In this sense the other

cross-cutting themes in this background paper (children, women and the elderly) are also

examples of stratified medicine.

A recent report of the United States National Academy of Sciences on the development of a

new framework for the taxonomy of human disease, however, advocates the term ‘precision

medicine’ for ‘the use of genomic, epigenomic, exposure, and other data to define individual

patterns of disease, potentially leading to better individual treatment’.6 While the term

‘stratified medicine’ reflects the realistic effect on patient/population-level, ‘precision

medicine’ reflects the clinical consequences — a better treatment. Both terms seem to reflect

more precisely the promises and hopes of the new genomic era than ‘personalised medicine’,

which might have been an overambitious definition promising individualised unique drug

targeting and development. In this report we will use the term ‘stratified medicine’.

Figure 7.4.2: Concept of stratified medicine. Biomarkers will enable us to target treatment

specifically to subpopulations of patients who are more likely to benefit from a particular

treatment.

Source: http://www.pharmainfo.net/reviews/role-pharmacogenomics-drug-development.7

http://www.pharmainfo.net/reviews/role-pharmacogenomics-drug-development


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3. From monogenic to polygenic approaches

In April 2003, the human genome project was declared complete with the sequencing of 99%

of the human genome.8 From 2003 onwards, data production in the field of genomics has

continued to grow exponentially, and instead of focusing on the human genome as an end in

itself, genomic researchers have turned their attention towards the role of DNA, RNA,

proteins, and metabolites in disease aetiology. As part of this trend toward more complexity,

interest shifted from the relatively rare monogenic diseases, which result from modifications

in a single gene, such as Huntington’s disease, cystic fibrosis and thalassaemia, to the more

common complex diseases, including cardiovascular disorders, type 2 diabetes, cancer,

depression, and Alzheimer disease.9

Simultaneously with the genomic revolution, the evolution of pharmacogenetics into

pharmacogenomics took place.10 Pharmacogenetics is the study of variations in a single gene

as related to drug response, whereas pharmacogenomics is often described as the whole-

genome application of pharmacogenetics where variations in multiple genes are assessed at

the same time. Although both terms are still used interchangeably, the term

‘pharmacogenomics’ is broader and also includes new genome-wide DNA technologies and

RNA.11

Successful early examples of pharmacogenetics involve identifying variations in genes

coding for drug metabolism enzymes which influence drug concentration and have major

effects on therapy response or the occurrence of side effects: thiopurine methyltransferase

(TPMT)10 and CYP2D6, for example. The enzyme thiopurine methyltransferase is involved in

the methylation of thiopurine drugs. Thiopurines are widely used to treat acute

lymphoblastic childhood leukaemia (ALL), inflammatory bowel disease, autoimmune

diseases and organ transplant recipients. Genetic variation in the gene coding for this

enzyme influences the enzyme activity. Approximately 10% of individuals have lower

activity for this enzyme (carriers of one gene variant: heterozygotes) and 0.3% lack enzyme

activity (carriers of two gene variants: homozygotes)12, and are very susceptible to severe

haematological toxicity when treated with thiopurine drugs (i.e. azathioprine and 6-

mercaptopurine) due to a slower metabolizing of the drugs. In 2004, the U.S. Food and Drug

Administration (FDA) recommended TMPT testing prior to the start of treatment with

azathioprine with a message on the product label of the drug, in order to enable the

adjustment of individual treatment dosages.

Cytochrome P450 2D6 (CYP2D6) codes for debrisoquine-sparteine hydroxylase, an enzyme

involved in the metabolism of xenobiotics, and is implicated in the oxidation of 20-25% of all

clinical drugs (including various antidepressants, antipsychotics and antiarrhythmic).13

Variations in the gene can lead to altered enzyme activity, based on the presence of these

variations individuals can be classified as: ‘poor’, ‘intermediate’, ‘extensive’ and ‘ultrarapid

metabolizers’. Approximately 7% of Caucasians are poor metabolizers, due to two variant

CYP2D6 alleles. These individuals may suffer from adverse drug reactions when treated

with standard xenobiotic drug dosages, or may not respond to drugs that require activation

by the enzyme. Conversely, ultrarapid metabolizers (estimated to be ~1% of the Caucasian

population14) may also suffer from adverse drug reaction due to the toxicity of prodrugs that

need to be activated by the enzyme, or a lack of therapeutic effect when treated with

standard drugs with a narrow therapeutic window. In 2004, a microarray based


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pharmacogenetic test, the Roch AmpliChip 450 test was approved for clinical use in the EU,

and in 2005 in the United States.15, 16 It has been used mostly in psychiatry, but is thought to

be valuable in the oncology field as well.13

CYP2D6 and TMPT both act according to a monogenic model where variation in one single

gene has a substantial influence on drug concentration and thereby drug effect. However,

with the rapid advances in genomics, it became increasingly clear that multiple proteins

participate in the metabolism of most drugs. Therefore, in order to evaluate the full

contribution of genetic variation in drug response, multiple genes in a drug signalling

pathway should be studied concurrently.10 This approach requires large numbers of cases in

order to have enough statistical power to identify the small effects of common gene variants.

4. Biomarkers and disease profiling

The summary report of the workshop ‘Stratification biomarkers in personalised medicine’

organized by the Directorate General for Research and Innovation of the European

Commission5 defined biomarkers as follows: ‘A biological characteristic, which can be molecular,

anatomic, physiologic or biochemical. They act as indicators of a normal or a pathogenic biological

process. They allow assessing the pharmacological response to a therapeutic intervention. A biomarker

shows a specific physical trait or a measurable biologically produced change in the body that is linked

to a disease or a particular health condition’. The report describes four distinct types of

biomarkers based on the purpose for which they are used: 1) diagnosis (in patients), 2) risk

assessment (in healthy individuals), 3) prognosis of disease (in patients) and 4) treatment

prediction (in patients) (Table 7.4.1).

Scientific literature shows an explosion in the discovery of biomarkers, however, only a few

have been validated for routine clinical practice.17 So far, most advances have been made in

the oncology field, in which molecular and genetic tumour profiling is increasingly used to

predict therapy response and/or prognosis. An important factor in the success of tumour

profiling is the development of targeted therapies in which drugs block the tumour by

binding to tumour-specific molecules. A classic case of a targeted therapy and associated

biomarkers is trastuzumab (trade-name: Herceptin®) and HER2 testing. Trastuzumab is a

chemotherapy agent that can block human epidermal growth factor receptor 2 (HER2). This

protein is encoded by the ERBB2 gene and the gene is overexpressed in approximately 15-

30% of the patients with breast cancer.18 Only patients with high levels of HER2 are likely to

respond to trastuzumab.19 In 2006, the FDA and European Medicines Agency (EMA)

approved trastuzumab for the treatment of HER2 overexpressing breast cancer and in 2010

for HER2-overexpressing metastatic gastric or gastroesophageal junction adenocarcinoma,20,

21 and HER2 testing has been imbedded in clinical guidelines.22 Another successful example

of a targeted anti-cancer therapy is imatinib (trade-name: Gleevec® (USA) or Glivec® (EU)).

The treatment blocks the working of a tumour-specific enzyme, which is often produced in

chronic myelogenous leukaemia and gastrointestinal stromal tumours. Blockage of this

enzyme inhibits tumour development and the activity of the enzyme is frequently monitored

in patients using imatinib in order to monitor the efficiency of the treatment.23


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Tumour profiling can also be used for prognostic purposes such as in the case of

MammaPrint® (or 70-gene strategy). The test assesses the expression of 70 genes from breast

cancer biopsies in order to predict the risk of reoccurrence of the breast cancer and to guide

treatment with adjuvant chemotherapy.24 It classifies patients into ‘low’ or ‘high’ risk for the

reoccurrence of breast cancer. MammaPrint® became available in Europe in 2004, and the

FDA cleared the MammaPrint® as a diagnostic tool for the United States market in 2007.25 A

large prospective multi-centre trial (MINDACT) with 6 000 breast cancer patients is being

performed to validate the guidance of treatment based on the combination of the

MammaPrint® and the establishment of standard clinical criteria is currently ongoing.26 Cost-

effectiveness models suggest a high likelihood of cost-effectiveness for the MammaPrint®

compared to standard clinical guidelines.27, 28 In a cost-effectiveness analysis based on three

MammaPrint® validation studies where MammaPrint® was compared to the clinically used

guidelines of St. Gallen and Adjuvant Online software, it was shown that MammaPrint® had

lower total health care costs per patients compared to St. Gallen (€28 045, respectively, €35

475), but higher total costs than Adjuvant Online Software (€26 915). Nevertheless,

MammaPrint® yielded more quality adjusted life years (QALY) (12.44) compared to

Adjuvant Online Software (12.20) or St. Gallen (11.24) 27. It should be noted that the gain is

limited and leads to the question of how much society is willing to pay for gained QALY’s.

Compared to the Adjuvant Online strategy the 70-gene strategy was estimated to costs €4

614 per QALY gained.

Table 7.4.1: Classifying biomarkers according to test outcome

Type of biomarker Example

Diagnostic Genetic testing of the Huntington’s disease gene in an individual with suggestive

symptoms in order to confirm diagnosis.

Susceptibility/risk Genetic testing of the Huntington’s disease gene in an individual with a family history of

the disease but without symptoms in order to assess the risk of Huntington’s disease

later in life.

Prognostic Assessment of the expression of a set of genes with the MammaPrint® in a tumour biopsy

of a breast cancer patient to assess the risk of reoccurrence of the disease and the need for

additional chemotherapy.

Predictive (for

treatment response)

Measurement of HER2 protein levels in the tumour biopsy of a breast cancer patient to

predict response to trastuzumab treatment

Source: European Commission, DG Research. Workshop report: Stratification biomarkers in

personalised medicine. 2010. Available at: http://ec.europa.eu/research/health/pdf/biomarkers-for-

patient-stratification_en.pdf . Accessed April 24.

Disease profiling and targeted therapies are not restricted to the oncology field. Disease

profiling and guiding of treatment also occur in other fields, including the treatment of

infectious diseases such as HIV29 and inflammatory diseases such as asthma, where

promising results are being seen for anti-interleukin-13 treatment lebrikuzimab. The drug

was successful in Phase 2 studies with asthmatic patients unresponsive to standard

http://ec.europa.eu/research/health/pdf/biomarkers-for-patient-stratification_en.pdf



7.4-12

glucocorticoid therapy, especially in patients with a disease characterized with high levels of

the serum protein periostin. Periostin is thought to be a surrogate biomarker for the level of

interleukin-13.30

In the case of lebrikuzimab, periostin can be measured in the blood serum of individuals;

nevertheless, a general limitation of disease profiling is the need of local ‘disease’ tissue,

which often requires an invasive procedure to measure locally produced proteins or

molecules. Pharmacogenomics, however, focuses on the genetic variations that influence

therapy response; these genetic variations are present in all tissue and can generally be

measured in DNA obtained non-invasively through mouth swabs or saliva.

5. Research

5.1 Study design

Generally, there are three types of epidemiological study design for pharmacogenomic

studies:31 1) randomized clinical trials (RCT), 2) observational case-control studies and, 3)

studies based on biobanks (potentially linked to electronic health records) (Figure 7.4.3).

Currently, Randomized Clinical Trials (RCT’s) are the gold standard in drug research (See

Background Paper 8.4). In a RCT, an individual is randomized to a specific

treatment/placebo or alternative medication, often using a double-blind method: neither the

study participant nor the researcher knows to which treatment the individual is assigned.

Due to the randomization and regular monitoring, bias and confounding are minimal. The

European Pharmacogenetics of AntiCoagulant Therapy-study (EU-PACT) is an example of a

pharmacogenomics RCT. This study, financed by the Seventh Framework Program (FP7) of

the EU, focuses on genetic variation and oral coagulation drugs (coumarin derivatives) in the

prevention of thrombotic disorders. These drugs have a narrow therapeutic window; the

dosage needs to be high enough to prevent blood clotting, yet not high enough to cause

severe bleeding. The correct dosage differs from person to person. Various factors, including

age, sex, diet and co-medication, as well as genetic variation (especially in the CYP2C9 and

VKOR genes) are thought to play a role. The EU-PACT study is an ongoing multi-centre trial

in 7 European countries and compares two coumarin dosages strategies: 1) dosing based on

clinical information and genotype, and 2) dosing based on clinical information alone

(standard care).32 In addition to the added clinical value, EU-PACT will also assess the cost-

effectiveness of this pharmacogenomics approach.

Another example is the recently published RCT based on prospective testing of genetic

variation in the ADRB2 gene in 62 children with chronic asthma. Asthmatic children

homozygous for the variant genotype seemed to benefit more from a leukotriene antagonist

than from a long-acting beta(2)-agonist. Children (homozygous for the variant genotype)

were randomized to a long-acting beta(2)-agonist combined with an inhaled corticosteroid

(LABA and ICS) or to a leukotriene antagonist combined with an inhaled corticosteroid

(LTRA and ICS). The ICS and LTRA group scored better on asthma symptoms and quality of

life, used less rescue medication and were absent fewer days from school when compared to


7.4-13

the group of children treated with LABA and ICS.33 These results are particularly interesting

since they demonstrate a genetic effect on the efficacy of existing medicines.

Figure 7.4.3: A visual display of the three primary epidemiologic study designs used in

pharmacogenomics: randomized clinical trials, case–control and biobanks.

Source: Ritchie MD. The success of pharmacogenomics in moving genetic association studies from

bench to bedside: study design and implementation of precision medicine in the post-GWAS era. Hum

Genet 2012; 131(10):1615-26.31


7.4-14

A RCT can only be performed when relevant genetic variations have already been identified,

and they are time consuming and costly. A more common and less expensive design that can

be used to explore relevant genetic variations is the observational case-control study.

Patients are enrolled based on their phenotype or drug response (i.e. efficacy, adverse events,

toxicity). Since information based on the drugs they have used is often collected

retrospectively, the majority of pharmacogenomic studies are appended to existing projects,

for example to a clinical trial for a new drug or new treatment regime in which DNA has also

been sampled. The ‘cases’ are the patients that have had an adverse drug reaction,

experienced drug toxicity or those that did not improve significantly. The controls are the

patients that did not experience an adverse drug reaction, did not experience drug toxicity or

did improve on the drug regime.

A pharmacogenomic case-control study can also be designed based on an observational

cohort study, such as the Pharmacogenomics of Asthma medication in Children with Anti-

inflammatory effects (PACMAN)-cohort study.34 This Dutch cohort study recruited children

that used asthma medication through community pharmacies. During a study visit, saliva

for DNA analysis and phenotypic information (including health care visits in the past year

and asthma symptoms in the past year) was collected. Cases and controls were subsequently

assigned within the cohort based on the collected information. In the PACMAN cohort

study, the cases were the children that had not responded well to asthma treatment (based

on symptoms scores or exacerbations despite treatment in the past year), while controls were

the children that had responded well to asthma treatment.35 A limitation of this type of

design is recall bias. Since the patient has to report symptoms, exposure, and toxicity

retrospectively, they might not always recall these events correctly. Furthermore, in

comparison to a clinical trial, there is less monitoring and control, and issues such as non-

adherence to treatment and dosage changes might lead to bias. However, this likely reflects

standard clinical practice.

A third and upcoming study design is based on a DNA-biobank (See Background Paper 8.5)

using data that is collected in a prospective or retrospective observational manner. An

increasing number of medical centres collect biological specimens (including DNA samples)

for research purposes and have these samples coupled to electronic health records. When

this information also includes drug exposure and phenotype outcomes (i.e. adverse drug

reactions), pharmacogenomics studies can be set up. An alternative strategy is to first

identify cases and controls in the electronic health database and subsequently go back to the

patients to obtain a DNA sample. An advantage of a biobank design is the large study

populations available. Nevertheless, studies based on this design are limited by the

information available in the electronic health records and in comparison to the other two

designs, there is little control over the phenotype information that is collected. In addition,

there are also large scale prospective longitudinal observational population cohort-studies

which involve extensive phenotyping of individuals, but do not originate from medical

centres with electronic health records (e.g. United Kingdom Biobank, Qatar Biobank). In

these studies, phenotype information is tightly controlled and standardized.

Observational studies and biobank studies are generally used to assess (currently unknown)

genetic/genomic variations, whereas RCTs are the current standard used to determine the

clinical value of newly discovered gene-drug interactions before implementation in clinical

practice. However, a RCT might not always be feasible, due to ethical or cost/resource

limitations. In the case of cost or resource limitations, an adaptive trial design could be a


7.4-15

desirable alternative. An adaptive trial enables the researcher to implement prior knowledge

(from observational studies or knowledge obtained during the early phase of the clinical trial

itself) to optimize the remainder of the trial, which, for example, could lead to adjusted

sample sizes.36

5.2 Defining Outcomes

A crucial step in pharmacogenomics research is defining the phenotype of interest or the

outcome of the study. Many aspects of therapy response may be of interest as the outcome.

Often pharmacogenomics studies focus on drug efficacy or toxicity; however, when

addressing these issues, there is a wide variation in the phenotypes being studied, which

makes comparability between studies difficult. It is also complicated to determine what

defines a successful drug response outcome (differences in drug dosage, length of time that a

patient is symptom-free, and different clinical measures of improvement), and study results

may differ based on the definition of outcome.37 Hence, there is a need for the

standardization of phenotypes of efficacy and adverse drug reactions.38

5.3 From candidate-gene approach to GWAS and whole-genome

sequencing

Within the field of genomics, there are two approaches to DNA analysis: a hypothesis-driven

approach (e.g. candidate-gene studies) and a data-driven approach (e.g. linkage studies,

genome-wide association studies, whole-genome/exome-sequencing). In a candidate gene

approach, the association between variations in selected genes and an outcome (i.e. disease

susceptibility, therapy response) is studied. Genes are selected based on biological

knowledge. For example, when studying drug-response, genes coding for proteins in the

drug-metabolizing pathway might be selected as interesting candidates. This type of study is

not designed to identify novel genomic areas that might be associated with the outcome, but

rather to confirm a hypothesized association between a gene and the studied outcome.

In contrast, data-driven approaches including genome-wide association studies (GWAS),

linkage studies and whole genome/exome sequencing are used to identify novel genomic

variants. Genes are not selected based on a priori knowledge in this approach, but variation

in the genome is studied in an unbiased context. Linkage studies have been used a great deal

in the genetics field in order to genetically map diseases. In a linkage study, genetic markers

are studied in families where a certain trait (e.g. disease) is inherited by several generations.

The key is to identify a genomic region that is always inherited by the affected family

members, but not inherited by the unaffected family members, and then narrow the genomic

region down as much as possible. These studies are appropriate for the study of

monogenetic traits. However, linkage studies are less suitable for drug response studies as it

is rare to find a family with a well-characterized drug response. Compared to traditional

genetic linkage studies, GWAS have higher statistical power to detect small to modest

genetic effects. In a GWAS many common genetic variations across the entire genome are

assessed in a large population of individuals to study whether any of the investigated

variations are associated with a phenotype of interest (e.g. disease susceptibility, therapy

response).39 The first GWAS was performed in 2005 on genetic variations associated with

age-related macular degeneration,40 and the first pharmacogenomics GWAS was performed

in 2008 on statin-induced myopathy. At present, over 1 300 GWAS have been performed

(Figure 7.4.4), and a list of all the published GWAS performed can be found at the website of


7.4-16

the National Human Genome Research Institute.41 The majority of GWAS have focused

mainly on the identification of disease susceptibility genes and far less on

pharmacogenomics, yet GWAS are increasingly being used in that area.42 GWAS on

treatment outcomes have mainly focused on drugs for which the dose needs to be

individualized or on drugs to which a poor response has severe clinical consequences.42 One

of the drawbacks of GWAS for pharmacogenomics is the large sample size requirements.

Expected genetic effect sizes are often small, especially when studying outcomes such as rare

adverse drug reactions, therefore it might be difficult to recruit large sets of patients.

However, it seems that the genetic effect size for drug response is often higher than those

reported for the genetic susceptibility for common complex diseases, and GWAS studies

studying drug response with small sample sizes and highly significant findings do exist.43, 44

Nevertheless, large international research consortia are required to increase samples sizes

and facilitate biomarker discovery and replication.

Figure 7.7.4: Number of GWAS published between 2005 and 2012

Source: A Catalog of Published Genome-Wide Association Studies. Available at:

http://www.genome.gov/26525384. Accessed January 9, 2013.41

Furthermore, GWAS is less suitable in capturing rare variants, as the GWAS arrays are often

designed to include mainly common genetic variants. Moreover, GWAS are very susceptible

to finding false positives due to multiple testing.45 Correction for multiple testing is common

practice in genomic research, however, it is complicated due to the existence of various

methods that all have their own limitations.46 Traditional solutions (such as the Bonferonni

correction) work well in settings were few genetic variants are studied, but may be too

conservative for GWAS settings. Balancing the risk associated with finding false positives

versus the risk that important genetic variants are missed (false negatives) remains an

important challenge.

http://www.genome.gov/26525384


7.4-17

Whole genome sequencing would be more accurate in assessing rare variants, though it is

still rather costly. The “1000 dollar genome” is expected to be realized in the near future47, as

sequencing costs are decreasing rapidly (Figure 7.4.5), yet currently it still costs over 1 000 US

dollars to sequence the complete DNA sequence of an individual. A cheaper and promising

alternative is whole-exome sequencing, whereby only the exonic regions of the genome are

sequenced. Exonic regions code for functional proteins and comprise approximately 1% of

the human genome. An important advantage of this strategy is that rare variants with larger

effects can be detected. However, current exome strategies do not cover all the exomes

present in the human genome, nor can they detect structural variants or chromosomal

rearrangements.48

An innovative approach, which combines the hypothesis-driven and data-driven DNA

analyses is the sequencing of 2 000 cancer-related genes of biopsies of cancer patients. In the

Netherlands, three cancer centres are collaborating in the Center for Personalized Cancer

Treatment (CPCT), and all patients with metastasized disease are asked to participate.

Mutations in 2 000 cancer-related genes are being assessed in order to identify predictive and

prognostic biomarkers.49

Figure 7.4.5: Decrease in costs per genome sequencing over time

Source: National Human Genome Research Institute. Available at:

http://www.genome.gov/sequencingcosts) Accessed April 23, 201350

5.4 Other -omics technologies

Pharmacogenomics is only one of the many -omics technologies that have emerged. All of

these technologies, to a greater or lesser extent, hold the promise of improving the prediction

of disease, the prediction of drug response and the phenotyping of disease.44 Advances in

these fields might provide valuable information about the biological pathways of disease and

health and drug response variability. We will now address some of the more promising -

omics technologies.

http://www.genome.gov/sequencingcosts


7.4-18

Pharmacoepigenomics is a relatively new field that focuses on environmentally-driven,

inheritable variations in the DNA structure and its effect on drug response.51 As it seems, not

only do variations in the sequence of DNA play a role in explaining heterogeneity to

treatment response, but variations in the physical DNA structure also play a role. The

structure of DNA is highly dynamic and regulates gene expression. Especially in cases where

there is a strong observed inter-individual heterogeneity in treatment response, but no clear

genomic association, epigenomic variations might be the missing link. Important epigenetic

mechanisms include DNA methylation, posttranslational modifications of histone proteins

and modulation of gene expression by noncoding RNAs.52 An example of

pharmacoepigenomics is the observation that hypermethylation of the WRN promoter in

colorectal tumours is associated with a clinical response to the drug irinotecan.53 Increased

methylation of the gene promoter leads to the silencing of the gene and hypersensitivity to

topoisomerase inhibitors and DNA-damaging agents such as irinotecan.

Transcriptomics investigates the transcriptome, the RNA transcripts produced by the

genome, and the regulation of that process. In order to translate genetic information into

proteins, DNA needs to be transcribed into RNA. Subsequently these RNA molecules can be

used to produce proteins. However, a cell also produces RNA molecules that do not code for

a protein (non-coding RNA), but still have regulatory functions within the cell. An important

project which also involves transcriptomics is the Encyclopedia of DNA Elements

(ENCODE) consortium. It focuses on the control of transcription by assessing regulatory

pathways and regulatory DNA elements, and it aims to characterize the genome from a

functional point of view. Current analyses include the study of 1 640 genome-wide datasets

from 147 different cell types,54 in order to gain more knowledge on the regulation of gene

expression in health and disease.

Proteomics assesses the proteome of the cells, and the regulation and production of all the

proteins in a cell. An alteration in the genome or transcriptome does not necessarily correlate

with an alteration in a functional protein; therefore, proteomic profiling may sometimes be

more accurate in predicting treatment response. Proteomics studies have, for example,

assessed the influence of chemotherapeutics on the dynamics of protein response in cancer

cells.55 Nevertheless, progress in proteomics has been limited thus far by labour-intensive

procedures and lack of high-throughput arrays.

Metabonomics (or metabolomics) assesses the effect of a systematic change in a biological

system caused by an intervention (such as drug administration or specialized nutrition).56

The assessment of the gut microbiome appears especially promising, since there is increasing

knowledge about the metabolic interactions between individuals and gut bacteria and the

effect on disease development, drug efficacy and adverse drug reactions.57

5.5 Replication and validation of findings

To validate pharmacogenomic findings, replication studies in different cohorts are essential.

Functional studies to assess the effect of the genetic variant on gene expression and protein

function are crucial as well. However, this might be complicated by the variances in outcome

definitions or phenotype descriptions. Furthermore, in order to profit optimally from

identified risk SNPs (single nucleotide polymorphisms), we have to understand causative


7.4-19

relationships and underlying biological pathways58, 59 and to understand whether the

identified SNP is a functional SNP or a surrogate marker.

Replication has become a standard practice in genomic research, stimulated by guidelines

produced by journals on how to report genetic association studies.60 Nevertheless, translation

of pharmacogenetic markers to clinical practice has been severely limited due to the low rate

of successful replication. Failure to replicate could be due to the overestimation of the effect

estimate and the use of an underpowered sample size, but it may also be due to the

underestimation of the influence of environmental factors.

5.6 Future diagnostics: from genotype to diagnosis

With the rapid advances in -omics technologies and the emergence of new biological

knowledge, it is becoming evident that the current way we classify diseases has become

outdated.6 Diseases with distinct molecular causes are still classified as one disease (e.g.

depression, lung cancer), while diseases that share a common molecular pathology are

classified into a multitude of different diseases. With emerging biological knowledge,

increased understanding of disease biology and the discovery of disease biomarkers, future

clinical practice will increasingly diagnose and treat patients based on molecular disease

mechanisms instead of, or in addition to, clinical symptoms. For research purposes there is a

need to understand phenotypic extremes and endophenotypes (surrogate endpoints that

reflect biological pathway activity). Furthermore, biomarker discoveries will most likely be

more successful in patient groups with a homogenous molecular disease background.

Patient selection before randomization in a clinical trial (‘enrichment’) to increase study

power, to identify patients with a greater likelihood of having the event of interest, or

patients that are more likely to benefit from the drug61, will increasingly be based on –omics

markers.62

6. What European collaborations and EU-funded initiatives exist?

There are two main European networks in the field of pharmacogenomics: the European

Society of Pharmacogenomics and Theranostics, which mainly focuses on the

implementation of pharmacogenomics, and the European Research Network

Pharmacogenetics/genomics, which mainly focuses on research. Additionally, The European

Personalised Medicine Association (EPEMED) is a not-for-profit organisation that aims to

bring global forces in stratified medicine together in order to harmonize stratified medicine

development and implementation across Europe, with a focus on diagnostics. It aims to

represent members from academia and industry, as well as patient groups and professional

service firms.63 Furthermore, various large international Europe-wide pharmacogenomics

studies are emerging (See Table 7.4.2).


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Table 7.4.2: EU-funded Initiatives and European consortia on stratified medicine

Study acronym Topic of investigation Funding

EU-PACT Pharmacogenomics of anti-coagulant therapy EU-FP7*

FLIP Progression of non-alcoholic fatty liver disease and the validation

of diagnostic and prognostic markers

EU-FP7

TINN Efficacy and safety of anti-infectious drugs in neonates EU-FP7

EuroTARGET Targeted therapy in renal cell cancer EU-FP7

BIOSTAT-CHF Pharmacogenomics and biomarker project on chronic heart failure EU-FP7

MoMoTx Diagnostics in kidney transplant patients EU INTEREG IVa

U-BIOPRED Biomarkers of severe asthma IMI**

IMI-DIRECT Patient stratification in type 2 diabetes IMI

IMI-SUMMIT Micro- and macro-vascular hard endpoints for innovative diabetes

tools

IMI

PADRE Pharmacogenomics of antidepressant response prediction EU-funded but

through national

funding authorities

ITCH International Consortium on Drug-induced skin and

hypersensitivity

Not EU funded

iDILIC International Consortium on Pharmacogenomics of Drug-induced

liver injury

Not EU funded

* EU-FP7: European Union’s 7th Framework program

** IMI: public-private Innovative Medicines Initiative

7. Genomics initiatives in low resource settings

Genomics initiatives have started to emerge in some low- and middle-income countries such

as Mexico64, India65 and Gambia66; nevertheless, for the majority of low- and middle-income

countries, stratified medicine seems to be far out of reach.67 At present, there is a lack of

technical and financial resources and infrastructure.

In 2005, a report from the Royal Society on stratified medicine68 advocated the development

and use of simple phenotypic tests in order to screen common genetic defects in order to

address medical needs in those countries. This idea is demonstrated in the case of glucose-6-

phopshate dehydrogenase (G6PD) deficiency, this disorder is common in malaria endemic

regions and causes severe anaemia after the use of (among other drugs) the anti-malarial

agent, primaquine. The gold standard in assessing the G6PD status of an individual is a

spectrophotometric assay of red cell G6PD content, but this requires a laboratory setting.69

Simple enzyme-based UV tests (‘fluorescent spot test’), which can be performed in the field,

are available, but they are not widely being used. According to a recent meeting report of the

WHO Malaria Policy Advisory Committee, implementation of such tests requires quality

control of the field laboratory results, as well as a cold chain to transport and store reagents.70

New point-of-care tests are currently under development.

In addition to G6PD, various other genetic factors are thought to influence the effectiveness

of anti-malarial agents, but there is a lack of pharmacogenetic data in low- and middle-

income countries, where they bear a disproportionate disease burden.71 In order to gain

knowledge, collaborations between parasitologists, national public health programmes and

genetic researchers should be promoted.71 Dried blood spots collected for efficacy studies


7.4-21

could be used for pharmacogenetic studies as well, as long as consent is obtained for

retrospective DNA testing.

We have to bear in mind, however, that the use of genetic testing might not be cost-effective

in low- and middle-income countries where basic health care is limited and appropriate

drugs are not always available. An alternative and novel approach to disease profiling is the

recently developed Xpert MTB/RIF® to diagnose tuberculosis (TB) and drug-resistant TB on a

molecular basis. It is a rapid test, taking less than two hours, that assesses the tuberculosis

genome in patient sputum samples with high sensitivity and specificity. However, a recent

cost and affordability analysis showed that the test is currently not financially viable in low-

income countries as long as funding for tuberculosis and cost reductions are not increased.72

Nevertheless, after a successful South-African pilot-study, the technology is currently being

rolled out nationwide by the South African National Department of Health. Xpert platforms

will be placed in all laboratories in nine designated high case-load districts and in all other

existing TB laboratories. It is estimated that the technology will substantially increase the

number of TB diagnoses, yet will also increase annual costs for TB diagnostics and

treatment.73 (See Background Paper Chapter 6.8 on Tuberculosis)

So far, most pharmacogenomics research has focused on Caucasians. However, clinically

relevant SNPs might be present in non-Caucasian populations, but be virtually absent in

Caucasians; a good example of this being the HLA-B*1502 allele. Individuals with this allele

have a higher risk for severe cutaneous allergic reactions caused by anti-epileptic drugs. The

allele is more prevalent in populations with Asian ancestry (10-15%) compared to Caucasians

(1-2%).74 The Pharmacogenetics for Every Nation Initiative (PGENI) was developed to

support pharmacogenomic research in low resource settings by developing and integrating

local risk data. This USA-based non-profit organization aims to sample 500 individuals from

every ethnic group that represents 1% or more of the population in 104 low- and middle-

income countries.71, 75 Another important initiative is the China Kadoori Biobank which has

collected data from over half a million individuals from ten geographically defined Chinese

regions.76 The researchers goal is to follow the individual’s health for at least two decades,

with a wide range of measurements have been performed, including the collection of blood

and DNA.

Particularly in low resource settings, (pharmaco)genomic approaches may hold promise in

addressing their limited health resources as efficiently as possible. The focus here should lie

on a better use of existing medicines.

8. Challenges for implementation

In order to successfully bring stratified medicine to the clinic setting, there are several

challenges to overcome, including the following: pricing and reimbursement, assessing its

clinical value, regulation of products, legal and ethical issues, dealing with orphan drugs and

patients, addressing the need for a well-organised technology infrastructure and public and

professional training.77


7.4-22

8.1 Pricing and reimbursement

In recent years, stratified medicine (drugs and associated biomarkers), also known as co-

dependent technologies or ‘joint products’ or ‘therapeutics with companion diagnostics’, has

gained more and more relevance, which poses certain challenges to the pricing and

reimbursement authorities. Three main points should be highlighted:

1. In Europe, most stratified medicine belongs to a group of high-cost medicines for which

public funding – either through hospital budgets or through the outpatient

reimbursement sector – can be a challenge for public authorities (See Background Paper

8.2 on Pricing and Reimbursement). One well-known example is trastuzumab, for which

European countries pay, on average, €600 per 150 mg powder vial (See Figure 7.4.6).78

Prices for medical tests, such as the HER2-positive breast cancer tests (FISH test), are not

publicly available and can be freely set by the manufactures. According to a study by the

Institute for Prospective Technological Studies, costs for the FISH-test (including material

and personnel costs) varied between €220 in the United Kingdom to €495 in the

Netherlands in 2006.79, 80

2. Stratified medicine combines the use of medical devices, such as tests, with medicines

and new reimbursement assessment procedures – often in the course of health

technology assessments –that take into account the whole treatment package that needs

to be developed. In a benchmarking report examining factors that support or hinder

market access to stratified medicine in reimbursement systems in European countries,

countries were ranked as supportive (e.g. Germany, the United Kingdom and France)

when reimbursement systems for the diagnostic and therapeutic were combined.

However, for other countries (e.g. Netherlands, Finland and Norway), no clear pathways

for evaluation and funding of stratified medicine were identified.81

3. Stratified medicine is at the juncture of the inpatient and the outpatient sectors, which is

especially relevant with regard to funding. A study undertaken in the framework of the

EMINet (European Medicines Information Network; to support policy makers of the

network of Competent Authorities of Pricing and Reimbursement (CAPR)) surveyed

funding models and pricing practices for the ‘treatment package’ of trastuzumab and its

accompanying diagnostics in 27 European countries, as an example of personalised

medicine.82 The results showed split funding in several countries; medicine expenses

were funded by third-party payers and the tests were paid for by the hospitals, which

further increased the pressure on hospital budgets. Furthermore, pharmacogenomic tests

were only reimbursed when clinical utility of the test was established. However, there is

little consensus on what evidence is needed for tests to have sufficient clinical value. 1

Some countries are countering this gap by establishing prescribing guidelines which

consider both the medical device and the medicine or interdisciplinary committees with

representatives from hospitals and third party payers.83


7.4-23

Figure 7.4.6: List prices of one 150 mg vial of trastuzumab at unit ex-factory price level in

EURO in thirteen European countries, 2005 and 2011.

AT = Austria, BE = Belgium, DE – Germany, DK = Denmark, EL = Greece, ES = Spain, FI = Finland, FR

= France, IS = Iceland, IT = Italy, NL = Netherlands, NO = Norway, SE = Sweden, UK = United

Kingdom. (Source for abbreviations: http://publications.europa.eu/code/en/en-370100.htm

UK: price paid by the National Health Service

Prices as of December 2005 and June 2011

Prices in Non-Euro-countries were converted with the corresponding monthly exchange rate as

published by the European Central Bank.

http://www.oenb.at/de/stat_melders/datenangebot/zinssaetze/wechselkurse/wechselkurse.jsp

Source: Pharma Price Information (PPI) service. Gesundheit Österreich GmbH (Austrian Health

Institute). 2012.78

8.2 Assessing clinical value

The assessment of the clinical value of a test or a marker demands the development of a

strategy for translational research in which evidence is gathered over time in order to allow

the incorporation of various tests into health care practice within a certain time frame, while

also assessing clinical utility and cost-effectiveness. Gathering evidence needs to take place

in several phases, similar to the phases of pharmacological research. A four phase framework

for translational research of genomic medicine was proposed by Khoury and colleagues.84

Phase 1 seeks to move a basic genome-based discovery into a health application: an example

is the construction of a genomic profile that predicts individual reactions to drugs. Phase 2

would lead to the development of evidence-based guidelines. A clinical trial that shows that

the adapting treatment to the genomic profile in a large group of study participants is

effective in avoiding side effects would fit into this phase. Phase 3 attempts to move

evidence-based guidelines into health practice. An implementation project to ensure that all

people in an entire country to whom a certain drug is being prescribed are first tested using

the genomic profile to determine their risk of side effects would fit in phase 3. Phase 4 seeks

to evaluate the ‘real world’ health outcomes of a genomic application in practice. The

evaluation of the occurrence of side effects to show that fewer people are affected after the

implementation of the genomic profile would be the phase 4 study of our example. It is

estimated that no more than 3% of published genomics research focuses on phase 2 and

http://publications.europa.eu/code/en/en-370100.htm

http://www.oenb.at/de/stat_melders/datenangebot/zinssaetze/wechselkurse/wechselkurse.jsp


7.4-24

beyond84, implying that evidence-based guidelines are rare in the field of genomics. An

additional example of the different phases of translation is given in Table 7.4.3 for HER2

testing and trastuzumab treatment.

Nonetheless, for cost, resource use issues (pharmaceutical industry might not be interested in

financing off-patent products) and ethical reasons (strong observational evidence of a high

risk of severe adverse events in patient subgroups) clinical trials will not be feasible for every

newly discovered biomarker-drug interaction. Yet the replication of findings obtained in

observational studies can be difficult, therefore strict guidelines are necessary to define

which evidence is required before a test can be implemented in clinical practice and whether

a clinical trial is needed. The following factors should be taken into account:85

Replication of findings in distinct study populations with diverse geographical

backgrounds and genetic ancestries

Severity of clinical event

Likelihood of clinical event

The prevalence of the marker across distinct populations (in the case of genomic

biomarkers)

Cost-effectiveness

Availability of clinical infrastructure, including a simple cost-effective clinical assay

In addition, the ease of implementation in a suitable clinical setting needs to be considered.

Will the biological samples need special handling conditions? Is the analytical test robust in

different (e.g. environmental) settings? Will the analytical technology be readily

implemented? Does the analysis require additional skilled personnel or can standard clinic

or clinical-lab staff perform it?

Table 7.4.2: Phases of translation with trastuzumab and HER2 testing as example

Description of phase Research question or activity

Phase 1 Discovery to health application How do cancer cells with high levels of the HER2 protein

respond to trastuzumab therapy?

Phase 2 Development of evidence-based

guideline

Develop guideline specifying to whom HER2 testing is

offered and to whom trastuzumab is prescribed in cancer

clinic in a research setting

Phase 3 Move guideline into health practice Instruct oncologists nationwide to use guideline

Phase 4 Evaluate health outcome Study survival rate of breast cancer patients after stratified

application of trastuzumab according to HER2 status.

Evaluation of quality of life of breast cancer patients. Cost-

effectiveness analysis.

Source: Vijverberg SJ, Pieters T, Cornel MC. Ethical and social issues in pharmacogenomics testing.

Curr Pharm Des 2010;16(2):245-52.86


7.4-25

8.3 Regulation of products

Regulatory authorities play an important role in the implementation of stratified medicine.

However, a 2007 meeting report of the Academy of Medical Sciences on optimizing stratified

medicine research and development outlined important pre- and post-approval barriers.87

According to the report, drugs and diagnostics are ideally developed simultaneously and

stratification of patients is taken into account during the drug development process, market

authorization process and reimbursement procedures. However, introducing stratification

prior to drug registration is complicated due to the different regulatory frameworks for

diagnostics and therapeutics in the EU, which is in contrast to the United States where

diagnostic and therapeutic approval applications to the FDA can be made simultaneously.

The European Commission has recently submitted a proposal for a new regulation to replace

the current Directive 98/79/EC on in vitro medical devices, which includes clinical genetic

tests.88,89 Other regulatory guidelines and reimbursement procedures should also be adapted.

There is a need to align regulatory processes between different regulatory agencies.

Furthermore, the lack of consensus on what evidence is needed to demonstrate clinical utility

and analytical validity of a diagnostic also hampers implementation, as well as the current

gold standard of the RCT (See section on study design).

Current regulatory guidelines on the role of pharmacogenetics in evaluating drug

pharmacokinetics differ on covered drug development phases, recommendation for DNA

storage and if a pharmacogenetics-related pharmacokinetics study is required or

recommended.90 Furthermore, the available regulatory guidelines seem primarily relevant

for drugs under development and not for registered drugs.

Pharmaceutical companies may have relatively little interest in assessing stratification post

drug approval, since this might reduce their return on investment, such as the case may be

when the use of the drug is shifted from an initial broad indication to a narrow, yet highly

effective, indication. However, flexibility in pricing could overcome this hurdle.87 From a

societal point of view, the inclusion of stratification on the drug label (testing prior to

prescription) might lead to the emergence of many new, small population subgroups, some

with rare genotypes (see section on ‘emergence of orphan populations’).

In order to enhance the understanding of the regulatory significance of genomic data, the

FDA initiated the Voluntary Genomic Data Submissions (VGDS) program in 2004 to allow

sponsors (industry, researchers) to submit exploratory genomic data voluntarily, without

immediate regulatory impact.91 In order to expand the program to non-genomic biomarkers,

the submissions have been renamed Voluntary Exploratory Data Submissions. This provides

the opportunity to discuss exploratory data on genomic and non-genomic biomarkers

between sponsors and regulators without the submission of an investigational new drug

application, a new drug application or biologics license application. Joint FDA-EMA VXDS

meetings have taken place in order to generate international consensus on the opportunities

and limitations of genomic and non-genomic biomarker data in drug development.

The FDA and the EMA both have a qualification process for drug development tools, which

include non-genomic biomarkers.92, 93 Plasma fibrinogen is one of the first biomarkers that is

currently progressing through the final stages of the new FDA DDT qualification review.

Increased fibrinogen levels are thought to be a marker of COPD patients at risk for

hospitalization and death.94


7.4-26

8.4 Legal and ethical issues

The new genomic era requires large numbers of participants in order to identify small risk

effects of common gene variants. Biobanks have emerged to store biological specimens and

genetic information, and data sharing is considered to be an essential part of the current

genomic research process. This raises new ethical and legal issues, concerning consent,

ownership and liability, for example.86

Participants in genomic studies have to give ‘informed consent’ to their participation in the

study and the use of their DNA samples. The text of the consent form also usually includes

possible implications for the research participant concerning issues such as privacy,

confidentiality and re-contacting. However, due to extensive data sharing, the emergence of

large-scale research platforms and the unique fingerprint nature of DNA, the validity of

privacy and confidentiality assertions are being challenged. When the genome of the

discoverer of the double helix, Dr James Watson, was sequenced, he agreed to release it to

public databases, except for the information about the gene apolipoprotein E. This gene is

known for its association with late onset Alzheimer disease. However, even Dr Watson did

not foresee that his genotype for apolipoprotein E could still be imputed with more that 99%

certainty based on his other genetic information.95 Furthermore, keeping genomic

information in complete anonymity is not technically feasible, nor desirable, should the

participant wish to withdraw from the study. Genomic data obtained from a research

participant may also hold information about family members who did not consent. In

addition, a better understanding of genomic information might bring clinical relevant

information that was not the aim of the primary study (‘ancillary or incidental findings’ – the

incidentalome96). For example, it has been shown that some genetic variants associated with

drug response can be associated with disease predispositions. How to handle the feedback of

this kind of additional information while still protecting the ‘right not to know’ remains a

challenge.97

In the context of privacy and confidentiality, the concept of ownership has also been much

discussed: who owns the genetic information of a study participant and which parties

should have access to this information? Most European countries have adopted genetic anti-

discrimination legislation in order to prevent misuse of genetic information by employers or

insurers. However, there is an ongoing debate as to how well these anti-discrimination laws

actually protect the privacy and confidentiality of individuals. A 2008 study by Van

Hoyweghen and colleagues showed that Belgian insurance companies still used genetic test

results or genetic information derived from physician records or insurance questionnaires

despite the existence of genetic anti-discrimination legislation.98 This practice was mainly due

to ignorance, confusion and misunderstanding, but was also the result of the lack of clear

legal definitions for ‘genetic data’ and ‘genetic tests’.

Implementation of pharmacogenomics in clinical practice requires that health care providers

are prepared for the sometimes new, and often complex legal issues that the introduction of

a new technology brings with it, for example, concerning liability. A pharmacist has a

professional duty to assess whether a certain drug type and dose is suitable for a patient.

When an adverse drug reaction, which could have been avoided by the use of a

pharmacogenomic test, occurs in a patient, the pharmacist could be liable, or in other words

legally responsible. Take, for example, coumarin anticoagulants. A major side effect of

coumarin anticoagulants is severe bleeding, with reported incidences of 1.5 to 5.0 per 100


7.4-27

patients a year.99 It has been shown that individuals who have variant alleles of the VKORC1

and CYP2C9 genotypes are at an especially increased risk of drug-induced bleeding.

Therefore, if pharmacogenetic testing for VKORC1 and CYP2C9 were to be made standard

care, and a pharmacist was to dispense warfarin in the absence of testing, the pharmacist

could be held liable if drug-induced bleeding occurred. However, the extent of liability

depends on accepted standards of care, as formulated in guidelines, and it is unclear

whether any pharmacogenomics application is currently considered to be ‘standard care’.77

Although there are pharmacogenomics tests used in clinical care, clinical practice guidelines

are rare, and labelling content is limited.100 The tests that are clinically available are mainly

restricted to the oncology, HIV, psychiatry and the cardiovascular field.101

8.5 The emergence of orphan populations

There is some concern that stratified medicine may lead to many new, small subgroups of

disease populations, some with rare genotypes. Pharmaceutical companies might find it

unattractive to invest in drug development for these small subgroups, and will instead focus

on the most prevalent genotypes. Patients with the rare genotype groups would then be

‘orphaned’ by the international therapeutic drug market. Drugs for rare diseases are known

as ‘orphan drugs’ (See Background Paper 6.19 on Rare Diseases). In the Netherlands, the

Dutch Steering Committee for Orphan Drugs launched an initiative in January 2009 in order

to encourage translation in the field of rare diseases by supporting companies in submitting

an Orphan Designation Dossier (ODD) to the EMA.102 Similar public private partnerships are

needed to stimulate research on rare genotypes. On the other hand, stratified medicine might

lead to a better use of existing medications. Drugs with severe, adverse drug reactions might

still be safely used in patients with a favourable genetic profile, and therefore be kept on the

market, a good example being the anti-HIV drug abacavir (Box 7.4.2: the abacavir case).

8.6 Technology infrastructure

Health information technology guides the exchange of medical information between patients

and providers. For a successful implementation of stratified medicine, a well-structured

health information technology infrastructure is required to enable standardized data

collection and to link pharmacogenomics data to clinical information systems in order to

facilitate surveillance and guide treatment decisions.77 In the Netherlands, the

Pharmacogenetics Working Party of the Royal Dutch Society for the Advancement of

Pharmacy is currently working to implement pharmacogenomics in their automated

medication control database in order to link pharmacogenomics test results to therapeutic

recommendations. These databases are used by the majority of general practitioners and

community and hospital pharmacists in the Netherlands.103 However, a major limitation is

the lack of genotyping data and there is limited evidence to justify prospective

pharmacogenomic testing. Furthermore, the infrastructure for genotyping is only available in

a subset of centres.103 In order to bring stratified medicine successfully to the clinic and

pharmacy counter, it is necessary to develop guidelines, register unfavourable gene variants,

and implement signalling systems that raise an alarm when drugs are dispensed for which

pharmacogenomics tests are required.


7.4-28

8.7 Education and training

The requirement of genomics or biomarker testing before initiating drug treatment is already

standard practice for some drugs (e.g. abacavir and trastuzumab) and will only increase over

the coming years. Nonetheless, a recent study of more than 10 000 American physicians

showed that less than a third had received education or training in pharmacogenomics.104

Health care providers, including physicians, pharmacists and nurses, should be better

prepared for clinical decision making by having adequate knowledge about the medicines

for which the patient should be tested.They need to be trained to interpret

(pharmaco)genomic data and test outcomes and to learn how to make clinical decisions

based on the data and to discuss genetic risks and stratified medicine with patients.

Education should not only be part of the basic training of health care professionals, but

should also be part of postgraduate courses to provide continuous training on new

developments. Several initiatives are emerging; the American Medical Association (AMA)

has developed a brochure for physicians and other health care providers to improve their

basic knowledge on pharmacogenomics.105 The United Kingdom Centre for Pharmacy

Postgraduate Education has developed an online learning course for pharmacists,106 and

pharmacogenomics is increasingly becoming part of the curriculum of pharmacy students.38,

107 Furthermore, pharmacogenomics is also included in the EU2P training program (the

European IMI Education and Training e-learning Master and PhD programme), funded by

the European Commission and the pharmaceutical industry.38 Another EU initiative is the

‘Fighting Drug Failure’, of the Marie Curie Initial Training Network that focuses on

pharmacogenomics. It is a three year PhD programme for selected international junior and

senior researchers.

Although pharmacists play a pivotal role in the implementation of pharmacogenomics,

educational tools should also be developed for other health care providers. There is a need

for standardised courses: educational courses for a broad range of health-care professionals

(clinicians, nurses, health care provider managers) in order to gain general knowledge and

promote implementation of stratified medicine in clinical practice, as well as in-depth

educational courses for researchers, clinical specialists and pharmacists. Furthermore,

database designers, biostatisticians and informaticians need to be trained in order to

optimize database building and data analysis. The public needs to be educated in order to

understand the possibilities and limitations of stratified medicine to prevent misplaced

anxieties or expectations and the development of mobile health care applications might play

a role in doing so.

9. Identified gaps and recommendations for research and policy

Stratified medicine will have a major impact on the health care system by stratifying patients

before initial treatment into groups of responders and non-responders, or patients with a

high chance of experiencing drug toxicity and patients with a low chance of experiencing

drug toxicity. It holds strong promise for enhancing drug safety and efficiency (of existing

drugs), as well as for the development of new drugs, drug targets and diagnostics. Despite

rapid technological developments and emerging collaborations, several gaps related to the

development, translation and implementation of knowledge remain.


7.4-29

Establishing a funded European research network

In contrast to the United States (www.pharmgkb.org), there is no funded research network

for pharmacogenetics in Europe. It would be beneficial to have a funded European research

network that can be the voice of the research community. This network could greatly assist

the European Union in identifying opportunities in research, strengthening collaborations

within Europe, as well as playing a role in standardization processes (for example phenotype

definitions) and organizing educational and scientific conferences.

Safer use of existing medicines is underserved

The main focus of stratified medicine lies in the development of new drugs, drug targets and

diagnostics. This focus is also reflected in the guidelines of the various regulatory agencies

for pharmacogenomics methodologies used in assessing drug pharmacokinetics, which

primarily concentrate on drugs that are currently under development. In addition,

pharmaceutical companies will probably be less interested in assessing stratified medicine

post drug approval due to pricing inflexibility and possible market loss. However, in both

low and in high resource settings, pharmacogenomics approaches to existing drugs may

hold promise for addressing health resources optimally. Pan-industry studies to address

stratified medicine with approved drugs should be stimulated, as should academia-initiated

studies in this area.

The effect of non-genomic factors influencing treatment response should not be

underestimated. So far, a large part of variability in treatment response cannot be explained

by genomic variations.108 Patient characteristics (such as age, gender, severity of disease),

gene-environment interactions, patient compliance, epigenomic regulation and protein

modification might also play an important role and should not be underestimated. Multi-

dimensional analyses in which biomarkers generated from different technologies

(transcriptomics, epigenomics, metabonomics) are combined with clinical parameters might

provide more insight in the pathogenesis, development and treatment response of

multifactorial diseases.

Need for a European catalogue of pharmacogenomic dataset and harmonization program

To validate pharmacogenomic findings, replication studies using different cohorts and

harmonization of outcomes is essential. A European pharmacogenomic database (to be

extended to a global database) with a comprehensive inventory of available phenotypes,

treatment data and other variables, as well as biological specimens, is lacking. Such a

database could make the replication of findings easier, leading to more international

collaborations and it could also identify those phenotypes/drugs where more data gathering

is needed. Altogether this will assist in the collection of more evidence, and, therefore, to an

increased chance of clinical implementation. An IT platform that will allow for data sharing

is therefore essential.

Current regulatory guidelines and reimbursement procedures hamper implementation

The regulatory frameworks of the FDA and EMA differ concerning applications of stratified

medicine and mainly focus on drugs under development. There is a need to harmonize both

regulatory processes between the various regulatory agencies, and also on the evidence

needed for clinical utility. A randomized clinical trial might not always be feasible due to

http://www.pharmgkb.org/


7.4-30

ethical considerations, a lack of resources or small populations. Clear guidelines are needed

to assess when a RCT is necessary to test a stratified medicine approach. Furthermore, an

adaptive trial design which enables the researcher to implement prior knowledge to

optimize the remainder of the trial, might be a cheaper and faster alternative to testing

observational findings. Cost-effectiveness studies are essential in assessing clinical validity.

In addition, product pricing frameworks should be reassessed in order to stimulate research

in the commercial sector on stratified medicine for registered drugs. Collecting and banking

genomic samples should become an integrative part of pharmaceutical, academic and/or

government funded RCT’s.

Stratified medicine in low resource settings is rare

A number of genomics initiatives are emerging in low resource settings; however, stratified

medicine approaches are rare. Especially in countries where resources are limited, stratified

medicine might be very successful in ensuring that limited health care resources are used as

efficiently as possible. Implementation of pharmacogenomics strategies of existing drugs in

low- and middle-income countries should be encouraged, taking their limited resources into

account. Furthermore, pharmacogenomics research in patients with different backgrounds

should be stimulated. This is also relevant to the situation in Europe, based on the large

changes in migration during the past decade.

Stratified medicine in vulnerable groups should be stimulated

Research should be funded to allow biomarker based prescribing during pregnancy and

childhood. The effect of distinct (genomic and non-genomic) predictors of therapy response

might be different in these subgroups and should therefore not be extrapolated from

pharmacogenetic-guided dosing algorithms developed for (non-pregnant) adults.109

Clinical added value should be assessed

Consensus should be reached on what evidence is needed for tests to have sufficient clinical

value. The assessment of the added clinical value of a test or a marker demands the

development of a framework in which clinical utility and cost-effectiveness are assessed and

compared to current clinical practice.

Barriers for implementation should be diminished

In order to prepare health care providers for clinical decision making based on stratified

medicine, there is a need for coordinated education and training programs, educational

courses for a broad range of health-care professionals and in-depth educational courses for

researchers, clinical specialists and pharmacists. Workshops involving both pathology

services and medicine therapy experts are necessary in order to guide the selection of those

medicines that require testing, as well as how test outcomes should be interpreted and how

dosages should be altered. Furthermore, database designers, biostatisticians and

informaticians need to be trained in order to optimise database building and data analysis,

and the public needs to be educated in order to understand the possibilities and limitations

of stratified medicine.


7.4-31

Ethical, legal and social implications of stratified medicine should be further investigated

The new genomic era brings along new ethical and social issues concerning genomic data

sharing, consent, ownership and liability. Furthermore, the level of protection by genetic

anti-discrimination laws and the societally acceptable costs of innovative drug therapies

should be investigated. Moreover, there is a lack of knowledge on the behavioural impact of

genomics-based risk assessments. All these issues should be further studied in order to guide

the implementation of stratified medicine in global health.

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