Using pharmacogenomics, researchers are able to understand how differences in genes affect responses to medications. By establishing a link between medication effectiveness and DNA, clinicians can predict whether a medication will be a good choice for a particular person and which dosage will help prevent negative side effects and adverse reactions. In practice, this means a healthcare provider may test for gene variants that affect drug response before prescribing treatment, allowing for more informed decisions from the outset.
In this blog, we discuss the crucial role genetic variations play in drug responses and why it is important to consider this in drug development.
Key Takeaways
- Definition: Pharmacogenomics studies how your DNA affects your response to drugs, moving healthcare from "one-size-fits-all" to personalized medicine.
- Safety & Efficacy: Genetic testing helps predict which medications will work best and which dosages will prevent dangerous adverse drug reactions.
- Biological Mechanisms: Genetic variations influence how drug receptors function, how drugs are taken up by cells, and how quickly the body breaks them down.
- Clinical Impact: Real-world applications are already in use for conditions like cystic fibrosis, heart disease (warfarin), and depression.
- Future of Pharma: Integrating genetic data into drug development can streamline clinical trials and reduce long-term healthcare costs.
How pharmacogenomics works
Researchers test which variants affect drug responses the same way that they test for variants linked with diseases: by pinpointing genetic sites linked to established drug reactions and subsequently examining individuals with unknown responses. To do this, they use multigene analysis or whole-genome single nucleotide polymorphism (SNP) profiles.
Organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) play a central role in translating these findings into clinically actionable guidelines, helping providers determine which gene-drug associations have sufficient evidence to inform prescribing decisions.
Within pharmacogenomics, there are two main determinants:
- Pharmacokinetics: how the body interacts with administered substances for the entire duration of exposure. Pharmacokinetics encompasses four processes: absorption, distribution, metabolism, and excretion.
- Pharmacodynamics: the study of a drug's molecular, biochemical, and physiologic effects or actions. Pharmacodynamics examines how drugs affect specific cells, like those in the heart or brain. These cells have various parts such as receptors, ion channels, enzymes, and immune system components that can be influenced by the drug.
Understanding both pharmacokinetics and pharmacodynamics is essential for tailoring drug treatments to an individual's unique genetic profile. Together, they form the basis for more personalized and effective medical interventions while reducing the risk of adverse drug reactions.
How genetic variations affect drug response
Researchers are still working to understand exactly how genetic variants affect the body's response to different medications. However, some gene-drug interactions have been extensively studied.
Warfarin sensitivity, warfarin resistance, and clopidogrel resistance are well-characterized examples. Warfarin, a blood-thinning medication, can exhibit varying efficacy and safety profiles depending on a patient's genetic makeup. Some individuals require lower or higher doses to achieve the desired anticoagulant effect. In the case of clopidogrel, a medication used to prevent blood clots, genetic factors can influence how well it inhibits platelet function.
These insights into gene-drug relationships not only advance our understanding of personalized medicine but also directly inform clinical practice.
DNA can affect multiple steps in the process of how a drug works in the body:
Drug receptors: These are proteins on the surface of cells that are essential for certain medications to function effectively. Their presence and quantity are determined by DNA, which is why genetic variation can directly impact drug response.
For instance, some breast cancers make too much HER2, a receptor, and this extra HER2 helps the cancer develop and spread. The drug T-DM1 works by attaching to HER2 on cancerous cells to kill them. A healthcare provider tests a sample of a patient's tumor to check the amount of HER2. If there is a high amount (HER2 positive), the doctor can prescribe T-DM1. However, if there is a low amount (HER2 negative), T-DM1 will not work for the patient.
Drug uptake: Some medications need to be continually taken into the tissues and cells they act on. A person's genetics can affect the uptake of drugs, meaning a medication may not work effectively or may build up in other parts of the body. DNA can also affect the speed at which drugs are removed from the cells they act on, which can significantly alter their efficacy.
An example of DNA influencing drug uptake involves statins, a common class of drugs that act in the liver to help lower cholesterol. Statins are transported into the liver by a protein made by the SLCO1B1 gene. Some people have a mutation in this gene that causes less simvastatin to be taken into the liver. For patients with this mutation, the drug can build up in the blood and cause muscle problems. For this reason, it is recommended for healthcare providers to check for the SLCO1B1 gene when treating high cholesterol.
Drug breakdown: Genetics may also determine how quickly a drug is broken down in the body. This can affect dosage as well as which drug may work best for the patient. An example of a medication that is affected by this would be the antidepressant drug amitriptyline, which is influenced by two genes called CYP2D6 and CYP2C19, both of which affect how quickly the body breaks down amitriptyline. A healthcare provider may therefore adjust dosage depending on the DNA of their patient to minimize adverse reactions and to make sure the drug is as effective as possible.
Personalized medicine in practice
While pharmacogenomics is still a relatively new field, there are many examples of how it has helped create better treatments already. For instance, the Food and Drug Administration (FDA) now includes pharmacogenomic information on the labels of around 200 medications to provide guidance on dosage, side effects, or differences in effectiveness for people with certain gene variants.
Today, pharmacogenomic insights are clinically actionable across a broad range of therapeutic areas, including cardiology, neurology and psychiatry, pain management, oncology, gastroenterology, and infectious disease. This breadth reflects the growing recognition that genetic variation influences drug response across nearly every area of medicine.
A specific real-world example can be seen in cystic fibrosis treatment (CF). Cystic fibrosis is a condition caused by mutations in the CFTR gene, which affects the CFTR protein. For most people, the CFTR protein forms a channel that acts as a passageway to move particles across the cells in the body. When that protein is made correctly, this channel can open and close. However, some individuals living with CF have a mutation that causes this channel to be permanently closed. A treatment for CF called ivacaftor acts on this kind of mutation to keep the channel open. Unfortunately, not all forms of cystic fibrosis are caused by these same mutations, so this medication only works for those with this type. By using genetic testing, doctors can make more informed decisions on CF treatment and offer ivacaftor to those with these specific mutations and know not to prescribe it to patients without.
Barriers to scaling pharmacogenomics
Expanding the implementation of pharmacogenomics presents clear advantages for patients, healthcare providers, and drug developers. However, meaningful barriers remain.
Current pharmacogenomic tests do not identify all genetic variations that may influence drug response, and results must be interpreted alongside other clinical factors, including age, comorbidities, and concurrent therapies. This means that PGx data alone does not determine the optimal treatment path. It provides one critical input within a broader clinical decision-making framework.
One promising avenue lies in enhancing data sharing and implementing standardized practices, which can accelerate the integration of pharmacogenomics into healthcare systems. Frameworks such as those developed by CPIC are helping to establish which gene-drug associations have sufficient evidence to guide prescribing, but broader adoption requires infrastructure, training, and interoperability across health systems.
Despite being a relatively new field, there is a strong case for incorporating pharmacogenomics into drug design and development as standard practice. By tailoring treatments to individual genetic profiles, pharmaceutical companies can enhance the efficacy and safety of their therapies while streamlining the development process. This reduces both the cost of development and the risk of adverse drug reactions. Over time, this approach has the potential to lower healthcare costs and improve patient outcomes at scale.
Conclusion
Drug responses vary significantly based on our DNA. Tailoring treatments to account for this is the most effective way to maximize efficacy, reduce wasted resources, and protect patient health. By identifying how patients are likely to metabolize medications before treatment begins, pharmacogenomics moves clinical practice away from trial-and-error prescribing and toward precision-guided decision-making.
By accounting for genetic variations, clinicians can better match medications to individuals, reducing adverse reactions and healthcare costs. While challenges in scaling remain, standardization and data sharing will accelerate the integration of pharmacogenomics into healthcare systems. Prioritizing this work in drug development and design will allow the field to continue evolving, paving the way for safer and more efficient therapeutic development.
For more on the use of genomics in drug development, download our whitepaper, Unraveling the complexities of genomics-driven drug discovery.