Site: EUPATI Open Classroom
Course: Role of Pharmacogenetics / Pharmacogenomics in the Development of Medicines
Book: Pharmacogenetics/Pharmacogenomics
Printed by: Guest user
Date: Monday, 17 June 2024, 6:34 AM

1. General Concepts

(This section is organised in the form of a book, please follow the blue arrows to navigate through the book or by following the navigation panel on the right side of the page.)

If we understand how variations in DNA and/or RNA between people affect how they respond to medicines, we can use this information to make treatment decisions.

Patients with the same disease can be separated into ‘sub-groups’ depending on their DNA and/or RNA. This can help doctors decide which medicine, and which dose, are likely to be most safe and effective for that individual.

This should also help doctors improve patient outcomes and reduce adverse side effects (adverse drug reactions, ADRs).

Much effort is put into research in this area with input from scientists in academia (universities) and in pharmaceutical companies. One example of this is ‘OncoTrack’. This project is looking at new approaches to help find new biomarkers for colon cancer.

There is a lot of discussion on ethical issues involved in pharmacogenomics, including:

See section below in this topic.

Gene expression

Gene expression measurements describe the level at which a gene is expressed (or present). For example, you can measure the expression of something called an ‘oncogene’. This can help researchers find out how likely an individual is to get cancer.

These measurements can be done with the final gene product, such as proteins. However, it is often easier to measure the ‘pre-cursors’, usually RNA. From that, you can then infer the level of gene expression. Gene expression measurements are included in the EMA definition of pharmacogenomics.

2. Stratified/Personalised Medicine

The purpose of stratified/personalised medicine is to use medicines that target particular sub‑groups of patients. This is discussed in more detail in the earlier EUPATI topic ‘The concept of personalised/stratified medicine’. Pharmacogenomics is one way to develop targeted medicines, and it will therefore be important to the progress of personalised medicine.

3. Variability in Response

Take a group of people that have all been prescribed the same medicine at the same dose. It is likely that some of these people will respond very well (full responders), but others will not benefit as much (partial responders). In addition, some individuals in the group will not respond at all (non-responders) and others again may suffer from major unwanted side effects (adverse reactions).

Individuals can vary in their response to a medicine both in terms of its efficacy (how well it works) and its safety (whether it causes side effects, or how well it is ‘tolerated’).

The principles of pharmacokinetics and pharmacodynamics are often summarised as:

Many differences between patients’ responses to medicines can be explained by differences in their pharmacokinetics or pharmacodynamics. Also, many genes influence pharmacokinetics and pharmacodynamics. Variation in any of these genes, or in their expression, can therefore be the root  cause of how people respond to a medicine.

A genetic test may provide information about how an individual will respond to a medicine. This means that the most effective medicine and dose can be chosen for this person, and the risk of side effects can be reduced.

For example, a recent study into advanced ovarian cancer has shown that it is possible to separate patients into two groups; 1) those who respond well to platinum chemotherapy and 2) those who do not. Platinum chemotherapy has several common side effects and some of them are serious. Knowing how likely a patient is to respond helps the doctor and the patient to assess whether it is worth risking these side effects. The gene expression ‘signature’ in this case is based on measuring the activity of 227 genes in the tumor. (1)

(1) Dondorp and de Wert 2013. The “1000-dollar genome”: and ethical exploration. European Journal of Human Genetics 21:S6-S26.

4. Examples of pharmacogenomics and pharmacogenetics in the clinic today

Doctors are using these new technologies to improve the efficacy of medicines and to get the optimal response in their patients. There are many examples of the successful use of pharmacogenetics and pharmacogenomics in clinical treatments. A selection is given below.

Example 1: reducing side effects (Adverse Drug Reactions, ADRs)
Possibly the most successful use of pharmacogenetics to date is the testing of HIV patients for a gene variant known as HLA-B*5701. The patients are tested for this gene before they are treated with the anti‑viral medicine ‘abacavir’. It is known that some patients (5 to 8%) will get side effects as a result of taking abacavir. The side effects can include skin rash, fever, gastrointestinal problems (stomach and gut), respiratory problems (breathing), and death. Because of this risk, some doctors have not been willing to prescribe the medicine even though it is an effective treatment for many patients with HIV. It was found that there is a link between adverse reactions and the presence of the HLA-B*5701 variant. Therefore, doctors can now test their patients for the variant before abacavir is given. There is good evidence that this testing has significantly reduced the number of adverse reactions.

Example 2: responders and non-responders

‘HER2 positive’ breast cancer cells have many extra copies of the gene that expresses the protein ‘HER2/neu’. This means that these particular cancer cells produce too much of the protein, and this causes the cells to grow and divide without control. The medicine ‘trastuzumab’ binds to the HER2/neu protein and this improves the overall survival for HER2 positive patients with advanced breast cancer (responders). The medicine does not have the same effect in patients who are not HER2 positive (non‑responders).

Example 3: dose choice

Warfarin is a medicine used to prevent abnormal blood clotting. Each patient’s blood is monitored when they are treated to ensure the correct level of warfarin has been given (enough to prevent abnormal clotting, but not so much that there is a risk of bleeding). In addition, there are guidelines now available for using pharmacogenetic tests for the enzymes known as ‘CYP2C9’ and ‘VKORC1’. CYP2C9 helps break down medicines in the body and VKORC1 plays a role in blood clotting (coagulation). The results from these tests can help the doctor choose the best start dose for any individual patient.

5. Clinical Trial Design

Many medicinal product candidates fail at each development phase and will not be taken further. Biomarkers, including genomic biomarkers (DNA and RNA measurements), should reduce this failure rate.

The most common reason for using genomic biomarkers in clinical trials is to improve patient selection. That is, only patients with certain DNA and/or RNA variations will be able to take part. The table below lists common methods for selecting patients for clinical trials using DNA and/or RNA variation, with examples.

Selection method


Exclude patients likely to have an adverse reaction .

Exclude HIV  patients who are at risk  of an allergic reaction because of the HLA-B*5701  genetic variant .

Select patients who are likely to respond (also avoid exposing other patients to a new medicine who are unlikely to benefit ).

Select melanoma patients with ‘BRAF ’ mutations for trials involving medicines called ‘kinase inhibitors’.

Select patients who have a particular disease sub-type (or severity) of interest in the trial.

Select patients with breast cancers that over-express the Her2/neu protein.

6. Novel Targets for Medicines

Researchers find new targets for medicines because they are getting better in understanding the link between genetic variation and disease.

For example, patients with cystic fibrosis have one of a number of variations (mutations) in a protein called ‘CFTR’. If the protein has a specific mutation known as ‘G551D’, the recently authorised medicine ‘ivacaftor’ can replace the function of this protein.

For patients with melanoma, the medicine ‘vemurafenib’ can be used to block the action of the mutated BRAF enzyme. This only works if the patient has the BRAF mutation known as ‘V600E’.

Within cancer many targets have been identified through pharmacogenomics and pharmacogenetics research. This is discussed in more detail in the following section.

7. Pharmacogenomics, pharmacogenetics and cancer

Pharmacogenomics and pharmacogenetics have probably progressed most in the development of cancer treatments.

New mutations (changes) that appear in tumour tissue in cancer genetics are known as somatic mutations (see section 7.1 below). Somatic mutations are the reason for tumour growth and are therefore the main focus of cancer genetics research. However, genetic variation in a patient’s normal tissue is also important in cancer therapy. Such variation is known as germline variation (see section 6.2 below). This type of variation is inherited – i.e. passed on from parents.

7.1. Somatic mutation

Cancer is widely seen as a genetic disease. If many mutations occur in certain genes, this can lead to tumour growth. The specific mutation(s) present in any one cancer can affect how it responds to treatment and cause it to be more or less aggressive.

If researchers understand these mutations, they can:

  • Develop new medicines that target mutated cell signalling pathways in tumours.
  • Find better ways to predict how a tumour will respond to a medicine.

A well-known example of a targeted treatment is the medicine ‘imatinib’ for the treatment of chronic myelogenous leukaemia (CML). CML is a rare blood cancer which is caused by a mutation in the tissue that produces new blood cells (bone marrow). The tissue changes lead cells to grow and devide out of control which causes a tumour. Imatinib targets the mutated protein (known as ‘Bcr‑Abl’) so that cell production becomes normal again.

7.2. Germline variation

Knowledge about inherited DNA variation in the patient’s healthy tissue (as opposed to new mutations arising within a tumour) can:

  • Help select the right treatment for the patient.
  • Help with decisions about dosing.
  • Predict the likelihood of adverse reactions from treatment.

For example, genetic variation can affect how a patient’s body breaks down (metabolises) a medicine. This can affect how well the medicine works. ‘Tamoxifen’ is a treatment for breast cancers that are known as ‘hormone-receptor positive’. Tamoxifen is converted in the body by the enzyme CYP2D6 into several molecules that target the tumour, including ‘endoxifen’. Some patients have a genetic variation that means they have low CYP2D6 enzyme activity, and therefore they produce less endoxifen. Levels of CYP2D6 have been linked to how well patients respond to tamoxifen. Other studies suggest that if the dose of tamoxifen is increased, it can help these patients and lead to higher levels of endoxifen in their blood.

Many treatments for chronic diseases may lead to side effects and some of these may be severe or serious. Research into how to use genetics to predict such severe adverse reactions is progressing. For example, recent research shows that the following can be predicted based on genetic information:

  • The risk of heart muscle damage from ‘anthracycline’ chemotherapy.
  • The risk of hearing damage from the medicine ‘cisplatin’.