In previous issues, we explored different aspects of personalised medicine. We started with the idea that through personalised medicines we could tailor therapies to individuals based on their genetics. We then delved into how a change in one chromosome, X or Y, can change a person’s response to medication. However, personalised is not always confined to an individual’s genetic makeup, and can be broadened to population genetics, and how this may influence the way individuals respond to drugs.
The idea that population genetics can be divided into 5 groups – African, Asian, European, Native American and Oceanian – is outdated and incorrect. This implies that genetic differences between races are large, and each race can be placed in a separate category. However, populations do cluster into geographical regions, and this can affect the genetic variation amongst humans. The variation between different regions is in fact small, and the lines between populations are blurred. Furthermore, even within a single region, the genetics vary and there often is no uniform identity.
A study done at Stanford University investigated whether differences existed in alleles across seven major geographical regions. Alleles are parts of genes that determine hereditary characteristics. For example, everyone has the same gene that will code for hair, however different alleles will give different hair colours. They found that 92% of alleles were found in two or more regions of the regions studied, and more than half of all the alleles were found in all seven regions. This indicates that even across different geographical locations, humans are much more similar genetically than they are different.
While there are indeed no “trademark” allelic markers or genetic features that are characteristic of a single group, these slight changes in alleles across populations influence both the risk of certain diseases as well as the populations’ response to medications and therapies. For example, the Ashkenazi Jewish population is at much higher risk of metabolic diseases and cancer than the general population. It is theorized that this is in part due to the “bottleneck effect” where the numbers of a population decrease drastically, leading to a reduced variation in the gene pool of that population. Another example is in German and British populations, where these populations are genetically predisposed to inflammatory bowel diseases such as Crohn’s disease and ulcerative colitis.
When it comes to therapeutics, there have also been several examples where differences in ethnicity have led to differences in responses to drugs. For example, propranolol is a very commonly used heart medication, mainly used to treat high blood pressure and to slow down heart rate. Differences in response to this drug between populations were first noticed in 1984, when a small trial found that in order to achieve the same effect of propranolol, a larger dose is required for African Americans when compared to Caucasian Americans. Warfarin, a commonly used blood thinner is another example where African Americans require a higher dose than Caucasian Americans, whereas Hispanic and Asian populations require a lower dose in order to have the same effect.
Although Warfarin concentrations in the blood of individuals on treatment are closely monitored, it has been speculated that even the therapeutic ranges may differ between populations. This becomes important when considering the therapeutic window of these drugs: if given too high a dose the patient is at risk of bleeding, and if given too little the patient is at high risk of blood clots which could lead to heart attack and stroke. Knowing the differences in response of different populations is therefore critical when treating patients.
The impact of genetic variation across populations on therapeutic response came to a head during the class action lawsuit against the pharmaceutical company Bristol-Myers-Squibb (BMS). This involved a drug, Plavix, which inhibits blood from clotting. It’s used as an alternative to Aspirin for preventing heart attacks and stroke. A large clinical trial comparing Aspirin and Plavix found that Plavix did indeed reduce the risk of stroke, heart attack and vascular death. However, 95% of the study participants that took part in that trial were Caucasian, therefore failing to take into consideration the genetic differences between populations that may affect the efficacy of the drug. Indeed, approximately thirteen years after drug approval, it was found that 75% of Pacific Islanders have a genetic makeup that make them unable to convert the drug into its active form, putting them at higher risk of strokes and heart attacks. This resulted in a class action lawsuit against the BMS, the company who makes Plavix. This class action lawsuit was ultimately won, with the Californian court ruling in favour of Plavix lawsuit proceedings, deeming that BMS falsely advertised the cardiovascular benefits of the drug.
So what is the solution? Is the answer to genetically test everyone that uses medications that so far have been found to act differently on populations with different allele variances? How can we avoid the discovery of harmful side effects of drugs on certain populations after the drug has been given approval?
As it stands, there continue to be issues with diversity in clinical trials. It is well known that minority populations have very low rates of participation. This occurs for a number of reasons like mistrust in research and fear of being a “guinea pig”, financial considerations of participating in clinical research, and lack of awareness and education of clinical trials. Including an adequate proportion of ethnically diverse groups in clinical research is a critical factor in helping the understanding of interactions not only between an individual’s genetic profile and the impact of this on therapeutic options, but also on the interactions between environmental exposures and social factors. The barriers to diversity in clinical research therefore need to be addressed.
There are ethical and practical issues associated with genetically testing individuals or populations of people in order to determine their response to particular therapies. First off, the issue of cost: it is estimated that including full genetic testing to clinical trials could add approximated $1 million to the cost of a clinical trial. This can put a barrier on genetic testing that could help to understand which populations may benefit, or not, from certain medications. Next is the matter of sample size. It is likely that in a given population, small percentages of individuals will have allelic differences that will result in them not responding to treatment. This may affect the statistical significance of those findings, and therefore may not in fact impact the drug development or treatment protocols.
The practice of pharmacogenomics (determining the likely response of an individual to therapeutic drugs) within populations also presents certain ethical issues. The definition of race or ethnicity in terms of genetics raises issues over whether developing the field of population-based therapies that build on past and problematic issues of racial biology will overcome the possible benefits of personalised medicine.
As encouraging as personalised medicine is, there are therefore several issues that need to be addressed. The inclusion of minorities within clinical trials, and funding for genetic testing in clinical trials are barriers that need to be overcome before population pharmacogenomics can move forward. The issues on race, population genetics, and pharmacogenomics also need to be addressed properly in order to reap the benefits of tailoring pharmaceuticals to different populations, without propagating inherent inequity that already exist.
Personalised medicine is a promising field that has the potential to eliminate improper dosage of drugs, individuals taking drugs that have no benefit to them, or to optimize treatments for different populations and individuals. However, proper research needs to be conducted, and care needs to be taken in order to ensure that all ethical and sociological considerations are properly weighted.