Precision Medicine has become a widely used term in recent decades, and one of the most significant advancements supporting its use is the field of pharmacogenomics (PGx). At its core, PGx involves the study of how an individual's genetic makeup influences their response to medications. This makeup is profiled by looking at the genes that encode the proteins involved in the absorption and metabolism of drugs. Based on these different genetic profiles we can better understand how an individual will respond to a particular therapeutic protocol. By tailoring drug therapies based on a person's genetic profile, PGx is transforming the landscape of medicine, leading to more personalized, more effective, and safer treatments. In this blog, we will explore how PGx is revolutionizing healthcare and improving patient outcomes.
The Growth of Precision Medicine
Traditionally, healthcare providers prescribed medications based on population averages, and assume that one-size-fits-all treatments will work for most patients. These population averages are typically established within clinical trials and may be assessed as the drug is more widely prescribed. However, individuals differ in their genetic makeup, which affects how their bodies process and respond to drugs. PGx enables an approach of personalized medicine, where treatments are tailored to an individual's unique genetic characteristics. This genetic profile is combined with environmental factors (such as smoking, nutrition, and poly-pharmacology) to determine more targeted therapies for that individual and help avoid adverse side effects and drug interactions.
One key genetic profile in PGx involves the genetic variations in the Cytochrome P450 genes. This is sometimes referred to as pharmacogenetics since it is solely based on the gene sequences, but most PGx panels include profiling the sequences of the seven different CYP genes that encode the P450 enzymes responsible for metabolizing over 90% of all drugs (Samer et. al. 2013). Profiling these gene variations has been used to identify four distinct phenotypes of drug metabolism.
Ultrarapid Metabolizers – their increased P450 activity may make them more susceptible to adverse drug responses, or mean they require higher drug doses for efficacy.
Extensive Metabolizers – have a typical response to drugs.
Intermediate Metabolizers – have decreased efficiency in drug metabolism, and possible decrease in drug responsiveness or increased sensitivity to side-effects of the parent drug molecule.
Poor Metabolizers – may retain higher levels of the parent drug, with no therapeutic benefit and increased risk of severe side effects.
Selecting the Right Drug for the Right Patient
PGx helps identify the most suitable drugs for a particular patient by analyzing genetic variations related to drug metabolism (a process that involves drug deactivation or drug activation) and to profile a patient as a rapid, intermediate, or slow metabolizer of a specific drug. This information aids in selecting both the appropriate medication and the dosage and importantly helps avoid potential adverse reactions or lack of therapeutic effects.
Adverse drug reactions (ADRs) are a significant concern in healthcare, often leading to patient harm and increased healthcare costs. PGx allows early identification of patients who are at a higher risk of ADRs due to their genetic predisposition. By empowering physicians and pharmacists to avoid medications or doses of medications that might cause severe reactions in their patients, PGx is set to improve not only patient safety but also overall treatment adherence through the improvement of drug efficacy and the reduction in side effects. Shockingly, severe drug reactions are responsible for more than 150,000 or 7% of hospitalizations every year, which has a staggering financial burden on our healthcare costs (Lazarou, et. al. 1998).
Enhancing Treatments Across Various Areas of Medicine
Oncology
Probably the area that most in the life sciences industry are aware of is how PGx has had a profound impact on cancer treatment; the so-called ‘cancer PGx‘ involves studying the variances of the genome that influence an individual’s response to cancer therapeutics. Furthermore, the genetic analysis of tumors enables oncologists to identify specific genetic mutations that drive cancer growth. This information aids in selecting targeted therapies that block or interfere with the tumor's molecular pathways, increasing the likelihood of successful treatment outcomes with the initial round of treatments, thereby significantly reducing the risk of drug resistance and leading to better patient outcomes. Similarly, the pharmacogenomic profile of the patient (and/or the tumor) also plays a crucial role in determining the optimal drug dosage for each patient. Genetic factors can influence the rate at which drugs are absorbed into the target tissues and metabolized before being eliminated from the body. By considering a patient's genetic variations, healthcare providers can adjust dosages and cadence of dosing to achieve the desired therapeutic effect without compromising safety, while minimizing the damage to healthy tissue.
PGx tests for specific tumor types are becoming more common as targeted therapeutics are approved. Irinotecan (Camptosar) is a type of chemotherapy used to treat colon cancer. However, individuals with a genetic variation resulting in reduced UGT1A1 enzyme activity cannot metabolize it to the active form effectively and are at higher risk of potentially life-threatening side effects. A simple UGT1A1 pharmacogenomic test can identify these individuals and help guide responsible treatment plans that involve either different chemotherapeutics or a lower dose of Irinotecan.
Similarly, patients with Acute Lymphoblastic Leukemia (ALL) are genetically profiled for their genetic variations in the enzyme thiopurine methyltransferase (TPMT) to determine if they are slow, average, or fast metabolizers of chemotherapeutics targeting ALL, and this information is used in dosing decisions.
Since many cancer patients may be prescribed multiple medications, it is important that the consequence of this is fully assessed before treatment begins. Combining multiple medications can lead to serious drug interactions that can be harmful or reduce the efficacy of treatments. Pharmacogenomic databases are being used to identify and flag potential drug-drug interactions based on a patient's genetic profile. This information allows healthcare providers to make informed decisions about potential drug combinations and help identify safer therapeutic intervention strategies and reduce the risk of adverse outcomes. To date, approximately 50 FDA-approved chemotherapeutic agents have package inserts containing pharmacogenomic information. For some of these drugs, such as Capecitabine (MYRIAD TheraGuide-FU) have mandatory pharmacogenetic testing associated with their use. But for most drugs currently, the FDA either recommends or proposes pharmacogenomic testing (Weng et. al. 2013).
Mental Health and Pain Management
Other areas of medicine that have benefited greatly from the implementation of PGx are mental health and pain management. Both areas often involve a trial-and-error approach to finding drugs that work for any given individual, and this can create a situation where patients decline while trying different drugs trying to find one that works for them. Genetic testing can help identify patients who are likely to respond better to certain psychiatric medications, and aid in poly-pharmacy decisions to address different aspects of mental health. Collectively, this is leading to more effective treatments for conditions like depression, anxiety, and schizophrenia (Jukic et. al. 2022).
Although not as mature as its’ use in oncology, mental health-based pharmacogenomic profiling is of increasing importance due to significant increases in mental health disorders during the Covid-19 pandemic that does not appear to be decreasing. The National Institute of Mental Health reports that an estimated 22.8% of adults and 49.5% of adolescents in the US are living (treated or untreated) with a mental health disorder (NIHM 2023). Currently, the success rate for antidepressant drugs is only between 42-53%, and with few advancements in psychiatry, many are turning to PGx to help improve treatment success rates and therefore improve patients’ lives.
Cardiology
Cardiovascular disease has been one area of medicine that has been significantly improved by PGx, although the translation of genetic variability into clinical practice has been complicated. Initial cardiac pharmacogenomic profiles focused on the CYP genes, but this has since been expanded to include genes for calcium channels, ACE receptors, and the β1 adrenergic receptor to name a few.
One of the first adoptions of cardiovascular PGx was in the assessment of patient suitability for anti-platelet therapies, such as clopidogrel, that are commonly used to prevent blood clotting. However, a significant proportion of the population carries genetic variations that affect the drug's activation, resulting in reduced effectiveness. A simple pharmacogenomic test allows physicians to identify individuals who may not respond well to standard anti-platelet therapies and select alternative drugs or dosages.
With cardiovascular care in the US costing over $320B annually and with prescription drug costs around $33B, there is a significant opportunity to cut costs and improve patients’ lives with this approach to precision treatment for cardiovascular disease (Mozaffarian et. al. 2016).
Final Thoughts
As promising as PGx is, there are however some challenges; PGx is highly complex, with multiple genes contributing to a specific trait or drug response. Understanding all of these interactions, particularly for new classes of drugs, requires extensive studies, and even then, doesn’t always provide a clear path to a clinically relevant PGx test. There is also a barrier with the education and adoption of this expensive and complex profile, and physicians are far less likely to use PGx if they don’t understand or trust it, and that can only be addressed with advanced clinical evidence and the implementation of PGx into routine clinical practice. There are also limitations in the diversity of the databases used to study gene variations, with most studies conducted in populations of European ancestry. Many ethnic groups are under-represented in these databases, and for whom this type of testing may provide limited value.
Despite these challenges, most agree that PGx is a transformative technology in the healthcare space, and while not perfect, can have a profound impact on healthcare costs, as well as the safety and health of many individuals around the world.
As PGx continues to advance, it holds the promise of transforming healthcare from a one-size-fits-all approach to a personalized and precision-based model, ultimately leading to safer, more effective, and individualized treatments for patients around the world. As science evolves, PGx is set to remain at the forefront of medical advancements, offering hope for a healthier and more tailored future in healthcare.
References
- Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998;279(15):1200–1205.
- Samer CF, Lorenzini KI, Rollason V, et al. Applications of CYP450 testing in the clinical setting. Mol Diagn Ther. 2013; 17(3): 165–184
- Weng L, Zhang L, Peng Y, Huang RS. Pharmacogenetics and PGx: a bridge to individualized cancer therapy. PGx. 2013 Feb;14(3):315-24. doi: 10.2217/pgs.12.213. PMID: 23394393; PMCID: PMC3605891.
- Jukic M., Milosavljevic F., Molden E., Ingelman-Sundberg M. PGx in treatment of depression and psychosis: an update. Trends Pharmacol Sci. 2022;43(12). doi: 10.1016/j.tips.2022.09.011
- National Institute of Mental Health. Mental Illness. https://www.nimh.nih.gov/health/statistics/mental-illness. Published March 2023. Accessed April 15, 2023.
- Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics-2016 update: a report from the american heart association. Circulation. 2016; 133:e38–e360.