Written by Dean Ihemesie
Precision medicine, a relatively newer approach to healthcare, can tailor treatment and prevention strategies to an individual's unique genetic profile, environment, and lifestyle. Instead of a universal approach, precision medicine acknowledges our individual biological differences, understanding that effective treatment for one person may not be optimal for another, it recognizes our biological diversity and the fact that treatment may vary from person to person.
Pharmacogenomics (PGx), a key factor of precision medicine, plays a major role in this new approach. PGx explores the relationship between an individual's genetic makeup and their response to medications. By studying how genes influence drug metabolism, efficacy, and the likelihood of adverse reactions, PGx paves the way for personalized drug selection and the ability for proactive instead of reactive dosage adjustments. This field aims to personalize medicine by tailoring drug choices and dosages to individual genetic profiles, thereby improving drug safety, enhancing drug development, and ultimately optimizing healthcare resources.
Despite the immense potential to revolutionize healthcare, precision medicine and pharmacogenomics face significant hurdles in their development and implementation. These challenges span various domains, including the complexity of integrating genetic data into clinical practice, the need for robust and cost-effective genetic testing, the ethical considerations surrounding genetic information, the requirement for educating both healthcare providers and the public about these advancements, etc. In this blog, we will address one hurdle in particular, Phenoconversion, and discuss its potential implications.
The results of a PGx test are typically represented as a phenotype, and these phenotypes are associated with specific genes that are responsible for drug metabolism, transport, etc. The potential metabolic phenotypes are as follows:
Phenoconversion is the discordance between an individual's predicted drug metabolism phenotype based on genetic variants and their observed phenotype in clinical practice (Klomp et. al. 2020). In simplified terms, it means that an individuals’ actual response to a drug may differ from what their genetic profile suggests. For example, a patient genetically identified as an ultrarapid metabolizer of a specific drug might exhibit a normal or even poor metabolizer phenotype due to phenoconversion.
A few mechanisms can contribute to this phenomenon, but concomitant medications that have drug-drug interactions (DDIs) can significantly alter drug metabolism. Concomitant medications with these interactions may inhibit or induce cytochrome P450 (CYP) enzymes, the key players in drug metabolism, thereby modifying the pharmacokinetic profile of the primary drug (Abouir et. al. 2024).
The CYP450 enzymes are implicated in the metabolism of at least 70% - 80% of drugs on the market. Additionally, lifestyle factors such as alcohol consumption, smoking, and even vitamin D exposure have all been suggested to influence the onset of phenoconversion of CYP450 metabolism (Zanger 2013). This is particularly relevant for drugs with a narrow therapeutic index, where even minor alterations in metabolism can lead to significant clinical consequences.
DDIs can occur when two or more drugs interact within your body, altering their intended effects. Populations such as elderly patients, or patients with an underlying disease, such as liver or kidney disease are at an even greater risk of having of having drug to drug interactions. Elderly patients are often polypharmacy, meaning that they take several medications each day, often considered as five or more medications (Klomp et. al. 2020). An individual's polypharmacy regimen proportionally increases the probability of pharmacodynamic or pharmacokinetic drug to drug interactions; this probability can escalate exponentially with each additional medication (Alhumaidi et al 2023).
In addition to polypharmacy, elderly patients are at increased risk just due to age. Some of the major players in drug metabolism are the liver and kidneys. As we get older, these organs don't work as efficiently as they used to, the amount of blood flow to these organs may decrease, and the enzymes that break down drugs may become less active. This means that drugs are cleared from the body more slowly, increasing the chances of side effects or harmful levels building up in the system leading to a higher risk of an adverse drug reaction occurring. An individual’s disease state can also be an underlying factor, experiencing liver and kidney disease are at similar risk to adverse drug reactions because the liver and kidneys are not functioning as intended (Shah et. al. 2015).
CYP2D6, a member of the cytochrome P450 (CYP) family is a key enzyme involved in the metabolism of approximately 25% of commonly prescribed drugs, including antidepressants, such as including selective serotonin reuptake inhibitors (SSRIs) like fluoxetine and paroxetine. The CYP2D6 gene is highly polymorphic, and genetic variations in the CYP2D6 gene can lead to significant differences in how individuals metabolize these medications, ultimately impacting treatment efficacy and the risk of increased side effects (Nahid et. al. 2023).
SSRIs are a common type of anti-depressant that work by blocking the reabsorption process, meaning that there is more serotonin readily available in the gaps between nerve cells, thus increasing the chances of serotonin binding to receptors on other nerve cells and continuing to send signals. Unfortunately, these drugs can also inhibit CYP2D6 activity, potentially leading to phenoconversion. This can result in increased drug levels and a higher risk of adverse effects.
Fig. 2. Phenoconversion of CYP2D6 in presence of strong (SI) or moderate (MI) inhibitors and metabolism of drugs by CYP2D6 phenotypes. (SI- strong inhibitors; MI- moderate inhibitor; gUM- genotypic ultrarapid metabolizer; gNM- genotypic normal metabolizer; gIM- genotypic intermediate metabolize; pPM- phenotypic PM; pIM- phenotypic IM; PM- poor metabolizer; IM- intermediate metabolizer.)
This inhibition occurs because SSRIs and certain other drugs can rely on the same enzyme, CYP2D6 for example, for metabolism. Essentially, they compete for the same binding site on this enzyme. Since SSRIs tend to have long half-lives, meaning they stay in the body for extended periods, they can effectively block other drugs from accessing the enzyme and being properly metabolized.
Warfarin is an anticoagulant drug with a narrow therapeutic index, meaning that even small changes in drug levels can significantly affect its efficacy and safety.
Warfarin works by disrupting the vitamin K cycle in the liver, which in turn reduces the activation of essential clotting factors. More specifically, warfarin inhibits the vitamin K epoxide reductase protein, leading to a decrease in the regeneration of reduced vitamin K, a crucial cofactor in the activation of clotting factors II, VII, IX, and X (Lurie et. al. 2010).
Fig. 1. Simplified diagram of the target of warfarin action and candidate downstream genes and effects. PharmGKB. (2024). Warfarin Pathway, Pharmacodynamics. Retrieved December 20, 2024, from https://www.pharmgkb.org/pathway/PA145011114
Genetic variations in CYP2C9 and VKORC1 genes can influence warfarin sensitivity. However, dietary intake of vitamin K, which is essential for blood clotting, can also affect warfarin response.
Patients with specific CYP2C9 and VKORC1 variations may be more susceptible to the effects of dietary changes, necessitating closer attention to their vitamin K consumption, and patients who consume a vitamin K-rich diet may require higher doses of warfarin to achieve adequate anticoagulation (Leblanc et. al. 2016)
Generally speaking, two factors contribute to DDIs - the pharmacokinetics and pharmacodynamics with the pharmacokinetic interactions being the most common. DDIs occur when a co-administered drug causes a change in absorption, distribution, metabolism, excretion, etc. (Muriel et. al. 2024).
Tolbutamide, a medication that is primarily used to manage type 2 diabetes, it works by increasing the insulin release from beta cells in the pancreas. However, the effectiveness of Tolbutamide can be significantly influenced by your CYP2C9 phenotype, mainly poor and intermediate metabolizers. Co-treatment with rifampicin, an antibiotic which also happens to be a CYP2C9 inducer, stimulates CYP2C9 activity, which can result in a two-fold increase in Tolbutamide clearance (Vormfelde et. al 2009).
In contrast, Clopidgrel, which is a blood thinner used to prevent platelets from forming clots is metabolized by CYP2C19. Co-treatment with a proton pump inhibitor in a patient who is phenotypically an ultra-rapid metabolizer will now be moved to a poor metabolizer status indicated by the loss of Clopidigrel efficacy (Depta et. al 2014).
As we have all come to realize, nothing we do is exactly black and white. Just because your pharmacogenomics profile shows you as one metabolic phenotype it may not necessarily be reflected in clinical practice due to so many other factors. Phenoconversion adds another layer of complexity to the already complicated puzzle known as drug metabolism.
While we broaden our understanding of precision medicine, the size of the hurdles we face in terms of development and implementation will begin to diminish. For example, initial challenges like the high cost of genetic sequencing and the lack of clear guidelines for interpreting genetic data are gradually being addressed through technological advancements and collaborative research efforts. Additionally, using the guidance of a pharmacist for assistance in the interpretation and applying the pharmacogenomic genotyping results to clinical practice is becoming more widely adopted. We will continue to move in the direction of precision medicine and address the obstacles as they come.
Back