In the last article we discussed the evidence to support the use of antidepressants and which ones seem to be the most useful. Following on from this one may wonder; do antidepressants work best in some people over others? In clinical situations this is common, as with all medication, some people respond better than others. But what factors can affect this variance and has it got anything to do with our genes.

That’s what we will try to explore here and touch on a future area of pharmacy known as Pharmacogenetics.

 

How do depression and anxiety rates vary between population groups?

It has been known for some time that women are at a higher risk of developing disorders such as depression and anxiety. Studies have found women to be diagnosed with depression or anxiety TWICE as commonly as men (1, 2, 3). There are multiple ideas as to why this may be; could women be reporting symptoms more? This certainly seems a sensible idea. Maybe men are developing other disorders such as substance abuse instead of presenting with depression. Again, another sensible idea. It could also be physiological differences down to genetic variation between the sexes, or most likely a malaise of many factors.

Another classification of population group could be ethnicity. The Mental health foundation states that BAME communities are more likely to be diagnosed with mental health issues. Obviously much of this is a reflection of different cultural and socio-economic contexts and the ability of the therapy to be culturally appropriate. One study did however find that those from a black ethnic minority background show a higher prevalence of psychosis than those from white majority backgrounds, even when correcting for socio-economic factors.

Whatever the reasons for these population variations even person to person difference takes place. A recent study also found a genetic link passed on in a family that affects a person’s susceptibility to severe recurrent depression (5). It goes without saying that environmental factors also play a huge if not far more important role in developing the diseases.

 

But what about variations in treatment response?

If we know that there is some population variance in the risk of developing depression and other mental health disorders. We could hypothesise that there may also be a population variance in response to antidepressants.

The largest study on gender variations with response to antidepressants was done in the U.S.A and was known as the STAR*D report. Here men and women were both treated for depression with Citalopram. The results found that women were 33% more likely to gain complete remission of their depressive symptoms as men, even when the researchers corrected for complicating factors (9). They did note that this shouldn’t be used as advice to only prescribe to women as there was a large positive result from the male cohort. But it is food for thought.

Furthermore, a small study looked at ethnic variations in response to antidepressant treatment with Citalopram/Escitalopram between a cohort of African-American and Caucasian patients. This found approximately the same 50% remission rate in both cohorts (1).

The STAR*D report as mentioned previously has now moved on to look at whether hormonal variations between men and women can account for the different response to SSRI treatment. Hopefully this will shed further light on the subject.

 

How can Pharmacogenetics play a part in this?

We have explored how there are population differences with depression rates and a different response rate to treatment with citalopram between genders. But Pharmacogenetics aims to be able to tell us which medicines are best to use in which patients using their genetic profile. Despite being a small field and one not practically used to make clinical judgements, there is already an interesting body of evidence waiting in the wings.

 

I have included a few examples bellow.

 

Citalopram: Here there is evidence relating to genetic variation manifests in different expressions of hepatic enzymes. CYP2C19 poor metabolisers were associated with a decrease clearance of citalopram compared with healthy individuals (6). The clinical implication of this is still unknown, but if you could find out, would it influence your clinical choices and discussions with patients?

 

Amitriptyline: There is evidence to suggest that people with a specific genotype, denoted here as TT are at an increased risk of adverse drug reactions leading to an increase risk of having to switch to another antidepressant (7). Again, if you knew this information when prescribing would it influence your decision making process?

 

The evidence in the arena of pharmacogenetics is still patchy, however it is sufficient in some areas to be practically put in to use. The issue then is genetic testing. The cost of the first sequenced genome (2003) is estimated to be somewhere between 500 million dollars to 1 billion dollars (8). By the end of 2016 it is estimated a high quality genome could be sequenced for around $1,000 (8). This is set to become increasingly cost effective.

 

Currently there are international groups working in the area, one being the Clinical Pharmacogenetics Implementation Consortium. Their goal is to address the difficulty of translating genetic laboratory test results in to usable clinical guidelines. The two pieces of evidence used above for Citalopram and Amitriptyline were both taken from their website. Moreover, they rank the evidence in terms of quality from level A to D. Level 1 A is the highest and forms information that could be used in clinical practice to affect prescribing. The evidence for Citalopram falls within this category. Currently the most usable data is on genetic variations in pharmacokinetics. They attest this can be used similarly to information on hepatic or renal function and dose adjusted as necessary.

 

There is also the Ubiquitous-Pharmacogenetics Consortium. This is a trans-European project that aims to address the major challenges for pharmacogenetic testing implementation across different healthcare settings. More specifically, if pre-emptive genotyping of a range of important pharmacogenetic markers results in cost effective improvements in patient care.

 

As you can see the two main challenges to pharmacogenetics are being studied, researched and invested in.

 

So could you imagine a time when you have your patient’s genome on hand and a database of genetic information relating to medications? Where you could pick the most genetically suitable medication for that specific patient; to increase efficacy, decrease side effects and improve cost effectiveness.

In the not too distant future this could be the reality of health care.

Author: MJ Burgoyne

 

References:

  1. Martin-Merino, E., Ruigomez, A., Wallander, M., Johansson, S. and Garcia Rodriguez, L. (2009). Prevalence, incidence, morbidity and treatment patterns in a cohort of patients diagnosed with anxiety in UK primary care. Family Practice, 27(1), pp.9-16.
  2. Nolen-Hoeksema S (1987), Sex differences in unipolar depression: evidence and theory. Psychol Bull 101(2):259-282.
  3. Bebbington P (1996), The origins of sex differences in depressive disorder: bridging the gap. International Review of Psychiatry 8(4):295-332.
  4. Kirkbride, J.B., Barker, D., Cowden, F., Stamps, R., Yang, M., Jones, P.B. & Coid, J.W.(2008). Psychoses, ethnicity and socio-economic status. The British Journal of Psychiatry, 193(1), 18–24.
  5. Breen G, Todd Webb B, Butler AW, et al. A Genome-Wide Significant Linkage for Severe Depression on Chromosome 3: The Depression Network Study. Am J Psychiatry Published May 15, 2011
  6. Pharmacokinetics of citalopram in relation to the sparteine and the mephenytoin oxidation polymorphisms. Therapeutic drug monitoring. 1993. Sindrup S H et al.
  7. Influence of the CYP2D6*4 polymorphism on dose, switching and discontinuation of antidepressants. British journal of clinical pharmacology. 2008. Bijl Monique J et al.
  8. Numbers from the national human genome research institute. https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost
  9. Young et al. Sex differences in response to citalopram: A STAR*D report. Journal of Psychiatric Research, 2008; DOI: 10.1016/j.jpsychires.2008.07.002
  10. Ira Lesser et al. Ethnic differences in antidepressant response: A prospective multi-site clinical trial. Journal of depression and anxiety. 27(1):56-62. January 2010.
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