People with the same diagnosis may react very differently to a prescription medication. The same drug may cure one set of patients, cause toxic side effects in another group and have limited effects in a third group. For many diseases and drugs, clinicians don’t have enough data to be able to predict whether a certain drug or dose will work in an individual patient. The field of pharmacogenetics aims to give clinicians this data.
Pharmacogenetics is the study of how gene variants can change drug metabolism in different patients. All humans have around 3 to 6 million gene variants in our genomes. We know that part of the individual variation in drug response is caused by these variants in our genes.
Using Pharmacogenetics to Provide Clinical Recommendations
One of the biggest challenges in pharmacogenetics is to translate reams of genetic data into meaningful clinical guidelines for healthcare providers. The Clinical Pharmacogenomics Implementation Consortium (CPIC) is an international group that provides peer-reviewed, evidence-based clinical prescribing guidelines based on gene variant data (1). CPIC guidelines are freely available on their website.
PharmGKB is a Stanford University project that provides gene-specific pharmacogenetic information tables and other pharmacogenetic resources (2).
Large-Scale Pharmacogenetics Study at the Estonian Biobank
The Estonian Biobank is a large-scale population biobank housing biological samples as well as lifestyle, demographic and medical data from over 51,500 participants (3). That’s almost 4% of the Estonian population.
Researchers from the Karolinska Institute and the University of Uppsala in Sweden recently teamed up with researchers from the Estonian Biobank to translate genotype data into pharmacogenetic recommendations (4). The researchers used genetic and genomic data from over 44,000 Estonian Biobank participants. They looked for variants in 11 genes known to affect drug metabolism: CYP2C19, CYP2C9, CYP4F2, DPYD, IFNL3, SLCO1B1, TPMT, UGT1A1, and VKORC1.
Genotyping Comparable to Sequencing
Out of the 44,000 samples used in this study, 41,289 had been genotyped using microarrays, 2,445 had been analyzed using exome sequencing and 2,420 had undergone genome sequencing. A small subset of samples (1,661) had been analyzed using more than one platform.
This meant that the researchers could directly compare how well the three different platforms—genome sequencing, exome sequencing and microarray genotyping—detect pharmacogenetic variants. Previous studies have shown that genome and exome sequencing can pick up rare variants much better than microarray genotyping. This is important because rare variants may contribute to as much as 30-40% of the individual variability in drug metabolism (4). One problem with rare variants is that there is often not much matching phenotypic or clinical data to show whether the variant is associated with any clinical changes in drug metabolism.
The Estonian Biobank study found that genotyping arrays gave similar results to genome sequencing, despite being much cheaper and easier to run. In contrast, exome sequencing missed several pharmacogenetic variants that lie outside of coding regions. The authors suggest, therefore, that microarrays could offer a cost-effective method to collect the data needed to make pharmacogenetic recommendations for individual patients. They also caution that exome sequencing is not the best technology for pharmacogenetic studies.
Translating Genetic Data into Pharmacogenetic Recommendations
The researchers then used CPIC and PharmGKB data to work out how detected variants should translate into drug dosing recommendations. They found that a staggering 99.8% of people tested in this study had at least one genetic variant that was associated with altered drug metabolism. For example, people with certain variants in the CYP2C19 gene metabolize certain drugs faster than the general population. These people are called CYP2C19 rapid and ultrarapid metabolizers.
Conclusion
This study used genetic and genomic data that was already available from the Estonian Biobank to look for genetic variants in 11 genes known to play a key role in drug metabolism. The data show that almost all of the 44,000 people tested have a genetic variant associated with altered drug metabolism. This means these people may need non-standard dosing regimes. This study shows the value of using large datasets collected and stored in biobanks to provide pharmacogenetic information. It also shows how important it is to dose patients based on their individual metabolism rather than on arbitrary doses based on collective data from clinical trials.
References
- Clinical Pharmacogenetics Implementation Consortium (Online) Accessed 30 October, 2018.
- PharmGKB: About Us (Online) Accessed 30 October, 2018.
- University of Tartu Institute of Genomics: Access to Biobank (Online) Accessed 30 October, 2018.
- Reisberg et al. Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions. Genetics in Medicine. 2018
