Making decisions about medicines (2)

Last updated: Tuesday, March 12, 2024

How pharmacogenomics can affect the risk of adverse drug reactions 

Adverse drug reactions (ADRs) account for around 1 in 16 hospital admissions. There are a number of different ways that genomics can influence an individuals’ susceptibility to ADRs. Understanding the underlying genetic causes of adverse effects and an individual's predisposition can help guide medicine choices in personalised medicine. 

 As part of this the MHRA and Genomics England are collaborating on the Yellow Card Biobank project. Currently this is focusing on bleeding with direct oral anticoagulants and severe skin reactions with allopurinol. 

Being aware of how a patient's genomics may affect their risk of ADRs is useful for all pharmacists and pharmacy technicians. Some examples of pharmacogenomic influenced adverse effects are given below. 

1. CYP-related effects 
CYP enzyme variants can influence safety of medicines as well as efficacy. Using an example from the previous page, as codeine is a pro-drug, which relies on metabolism by CYP2D6 to its active metabolite morphine, increased metabolism can lead to increased levels of morphine. Ultra- rapid metabolisers of CYP2D6 convert more codeine to its active metabolite and there is an increased risk of toxicity from increased morphine levels. 

Some medicines specifically require genetic testing for CYP enzymes before treatment is started with the results influencing the dose or whether the drug is contraindicated. 
Siponimod is a specialist drug used in the management of secondary progressive multiple sclerosis. It is extensively metabolised by CYP2C9 (79.3%) and to a lesser extent CYP3A4 (18.5%).

Patients who have specific poor metaboliser statuses for CYP2C9 have much higher plasma levels (up to 284%) of siponimod resulting in increased risk of adverse effects. Before initiation of treatment, patients must be genotyped for CYP2C9 to determine their CYP2C9 metaboliser status.

Based on these results siponimod may be;
• contraindicated (CYP2C9*3*3 genotype)
• a lower dose may be recommended (CYP2C9*2*3 or *1*3 genotype)
• or the standard dose may be appropriate (all other CYP2C9 genotypes)

2. Human leukocyte antigen 
The human leukocyte antigen (HLA) system is a complex of genes involved in the regulation of the immune system. HLA genes are highly polymorphic, vary widely across populations and several variants have been associated with immune adverse reactions to various drugs. They are particularly implicated in dermatological reactions such as Stevens-Johnson Syndrome (SJS), toxic epidermal necrolysis (TEN) or drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome. Drugs associated with HLA-mediated adverse effects include carbamazepine and allopurinol. 
Abacavir is a nucleoside reverse transcriptase inhibitor (NRTI) used in the treatment of HIV. It is associated with a serious immune-mediated hypersensitivity reaction that occurs in 5-8% of patients and requires the immediate cessation of therapy.

It has been shown that this hypersensitivity reaction is more common and more severe in patients with the HLA-B*57:01 allele.

Testing for this HLA allele is mandated for all patients who may benefit from starting abacavir. Patients with a positive HLA-B*57:01 allele can then be offered alternative therapy without risking a severe adverse effect or having effective treatment delayed.

3. Mitochondrial mutations 
Changes in mitochondrial DNA are another way that genomics can alter a patient's susceptibility to adverse effects. One example, as outlined in a 2021 MHRA Drug Safety Update, is where rare mitochondrial mutations have been associated with an increased risk of deafness when patients are given aminoglycoside antibiotics. The most common of these is the MT-RNR1 m.1555A>G variant which has an estimated prevalence of 1 in 500 in the UK population. 

4. Other examples of pharmacogenomic-mediated adverse effects 
SCLO1B1 gene variants can alter the risk of developing myopathy with statins.

Information technology, data transfer and clinical responsibility

As pharmacogenomic testing services develop, new questions will arise for healthcare systems. Future things to consider as genomic knowledge and these services develop;
  • Where would this information be held? 
  • What about information transfer? 
  • Who would be able to see any results/genomic information? 
  • Would this information have any relevance to existing treatment? 
  • Will pharmacogenomic information be incorporated into prescribing systems? 
  • What happens as testing improves?  
  • Would existing results need to be retested or reclassified? 
  • Who would be responsible for reviewing this data?