ADRs are argued to account for 6.5% of all hospital admissions (Pirmohamed et al., 2004) and are identified by the left-wing pressure group Compass (2008) as costing the NHS £2 billion annually. Elsewhere, Lazarou (1998) identifies ADRs as the fourth leading cause of death in the USA. As such, the need to understand the factors which influence drug response variation is a ‘critical issue’ (Beard and Lee, 2005: 2).
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There are a number of physiological, environmental, individual and genetic factors which can cause variation in drug metabolism such as liver, lung or kidney function; alcohol intake; smoking; age; sex; body fat and genetic polymorphisms (Gibson and Skett, 2001). Within this, there are two categories of ADRs; type A reactions are common, can occur in any individual and can be controlled through dosage adjustment (Severino and Zompo, 2004) whilst type B reactions (also known as ‘idiosyncratic’ reactions) are uncommon and strongly related to genetic variation (Uetrecht, 2007).
These type B drug reactions occur as a result of chemical or structural genetic polymorphisms, the most common form of which are single nucleotide polymorphisms (SNPs- pronounced ‘snips’) which account for 80% of all known genetic changes (Jain, 2009). SNPs are allelic variations at the site of one base pair which affect the encoding function of the gene, which affects the protein which is expressed and, thus, can result in a phenotypic variation (Jain, 2009). As well as SNPs, insertions and deletions (INDELS) of DNA and copy number variation (CNV) are also responsible for type B ADRs. Briefly, INDELS are the insertions or deletions of segments of DNA which, when occurring on encoding genes, can result in a ‘frameshift’ meaning different proteins and, subsequently, different phenotypes are expressed. Jain (2009) notes that if one understands the human genome as an instruction book, SNPs are the equivalent of altering single letters whilst INDELS are analogous to inserting or deleting whole sentences or paragraphs. Finally, CNV refers to a deviation from the normal human diploid (two copies of each gene) genome potentially resulting in INDELS or duplications of DNA. This can then cause over- or under-expression of protein which, in terms of drug response, is phenotypically manifested as either non-responsiveness due to abnormally quick metabolism or an ADR due to inadequate metabolic activity (Jain, 2009).
Studies of genetically-determined drug response variability tend to focus on chemical SNP variations rather than structural INDELs or CNV. Jain (2009) notes that this focus on SNPs tends to overlook the importance of structural variations, which Korbel et al. (2007) suggest may be responsible for most human genetic variation. Nevertheless, what is demonstrated by this genomic understanding of ADRs is that molecular variability is central to the ADR story.
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3.2.1. Terminology
Pharmacogenetics, then, is centred on mobilising the data emerging from these molecular understandings of ADRs and using it in order to manufacture safe and effective drugs and making safe and effective prescription decisions. Within this, there is an ontological distinction between pharmacogenetics and pharmacogenomics where the former is widely understood to refer to single genetic changes whilst the latter focuses on protein expression in a whole genome. However, there have been, and continue to be, debates within the scientific community about the definitions of these two terms (see European Agency for the Evalutation of Medicinal Products, 2002).
Table 2 shows Jain’s (2009) characterisation of the differences between pharmacogenetics and pharmacogenomics vis-a-vis a number of scientific and clinical features (also see Lindpaintner, 2003). Here Jain (2009) demonstrates the focus of pharmacogenetics as individual gene variability and pharmacogenomics as whole genome protein expression. As such, the techniques employed in pharmacogenetics are most commonly associated with clinical practice whilst pharmacogenomic techniques are more commonly associated with drug discovery processes.
Table 2: Differences between pharmacogenetics and pharmacogenomics. Taken from Jain
(Jain, 2009: 70).
Feature Pharmacogenetics Pharmacogenomics
Focus of studies Patient variability Drug variability Scope of studies Study of sequence variations
in genes relevant to drug response
Study of the entire genome
Methods of studies SNPs and expression profiles Gene expression profiling Relation to drugs One drug, many patient
genomes
One patient genome, many drugs
Examination of drug effects Study of one drug in vivo in sample of patients with inherited gene variants
Study of differential effects of several compounds on genome in vivo or in vitro Prediction of drug efficacy Moderate High
Prediction of drug toxicity High Moderate Application relevant to
‘personalised medicine’
Patient or disease-specific healthcare
Drug discovery and development or drug selection
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It is noted elsewhere that the terms pharmacogenetics and pharmacogenomics can be interchangeable since they are both concerned with the same broad area of research (Pirmohamed, 2001; Wieczorek and Tsongalis, 2001). This inter- changeability has led to the conflation of these two terms under the umbrella heading ‘PGx’, which is defined as ‘collectively …the science and technologies associated with dividing patients or populations into groups on the basis of their biological response to drug treatment using a genetic test’ (Hopkins et al., 2006: 403). This has also been called ‘the new pharmacogenetics’ (Pfost et al., 2000) and ‘pharmacogenetics-pharmacogenomics’ (Wang, 2010).
Alongside these somewhat technical terms, Liggett (2001) notes that less formal terms have also been applied to this paradigm of research and practice. Hence, terms such as ‘personalised medicine’, ‘stratified medicine’ and ‘tailor-made treatments’ have become synonyms for pharmacogenetics/genomics in more popular literature. Hedgecoe (2004) notes that whilst such definitions can be useful for conveying the core approach of this area, they can create a disparity between patients’ and practitioners’ operationalisation of ‘personalisation’; whilst practitioners may understand it relative to existing blockbuster models, patients may understand it as a wholly tailored drug programme. Hedgecoe and Martin (2003: 515) also note that the ascription of a name to a scientific discipline is not an arbitrary or accidental process and argues that ‘the names we use to label particular disciplines have a role in structuring them, and this in turn affects the uptake of particular technologies’. Pharmacogenetics and pharmacogenomics are examples of this, where pharmacogenomics first appeared in the scientific literature in 1997 and utilised the interest and hype surrounding the more general field of genomics which existed at this time. As such pharmacogenomics was discursively posited as a ‘natural or evolutionary development’ of pharmacogenetics due to its utility of contemporary genomic techniques (Vesell, 2000: 119)
For the purposes of this thesis, pharmacogenetics and pharmacogenomics are conflated under the heading ‘PGx’ given that many of the nuances between the two are not likely to be significantly relevant in the context of everyday pharmacy practice. Hence, whilst most pharmacists’ interaction with PGx is likely to involve pharmacogenetic technologies and data, pharmacogenomic principles are also likely
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to be present, not least because the most commonly used PGx test (HER2 testing) is pharmacogenomic in nature. However, most pharmacists’ interaction with PGx technologies is likely to be in the form of black-boxed testing devices and/or binaried output data where the scientific methods (i.e. whether these results are arrived at through protein or gene tests) underpinning them are not visible or necessarily relevant for organising PGx work activities. Where the distinction between pharmacogenetics and pharmacogenomics is relevant, however, this is made clear.