Via Research Recognition Day 2024 VCOM-Carolinas

Biomedical Studies

PROPOSED MEDICATION ALGORITHM FOR SELECTED OPIOIDS BASED ON CYP450 2D6 PHARMACOGENETIC VARIATION Barrie Clark, OMS-III, Steven Enkemann, PhD. Edward Via College of Osteopathic Medicine, Dept. of Cell Biology, Spartanburg, SC.

Abstract

Figure 1: Opioid metabolism and active species of analgesic potential.

Context: Opioid medications play an important role in the treatment of a variety of medical conditions by attenuating pain sensation. Despite the analgesic benefits, opioids have many adverse side effects, including respiratory suppression, sedation, dizziness, nausea, vomiting, tolerance, and physical dependence. Currently, medical professionals rely on a trial-and-error approach to find the most effective opioid to prescribe to their patient. However, this approach can be both time consuming and risky for patients. Individual genetic variation contributes to clinical effectiveness and risk for adverse events. Pharmacogenetics show promising advancements in accurately prescribing these medications to improve analgesic response and reduce adverse side effects. Pharmacogenetic testing categorizes patients into ultra-rapid (UM), normal (NM), intermediate (IM), and poor metabolizers (PM) depending on the activity score of their enzymes. By understanding each genotype’s response to opioid pain medications, a stream -lined approach to prescribing appropriate analgesia may be developed. Objective: This work aims to compare the current knowledge about genetic variation in the metabolism of selected opioid drugs and offer a proposed treatment algorithm based on CYP2D6 PM or UM genotype. Methods: A literature search was conducted to understand the current knowledge of opioid medications and pharmacogenetic influence on their clinical effectiveness. PubMed, Google Scholar, the Clinical Pharmacogenetics Implementation Consortium (CPIC), and the Dutch Pharmacogenetics Working Group (DPWG) were utilized to understand the metabolism, mechanism of action and clinical response to opioid medications. Selected opioids were pulled based on a pre-developed pain management algorithm. Results: Based on the current literature, the metabolism, mechanism of action, and properties of oxycodone, hydrocodone, morphine, and hydromorphone are well understood, however, ketamine and hydrocodone-acetaminophen are less well understood. The analgesic response in specific CYP2D6 genotypes (PM or UM) is also not well delineated. Considering the current knowledge regarding the mechanism of action, metabolism, properties and clinical response to each of these selected opioids, we propose the following analgesic treatment algorithm for CYP2D6 PM: oxycodone, hydrocodone/hydrocodone-acetaminophen, morphine, hydromorphone, and ketamine. In CYP2D6 UM, we propose utilizing morphine, hydromorphone and ketamine due to the unknown adverse events these patients may have to oxycodone and hydrocodone. Conclusion: Genetic variation impacts the metabolism of opioids. Specifically, CYP2D6 is a major enzyme involved in the metabolism of oxycodone and hydrocodone to more potent analgesic compounds. Significant advancements have been made in understanding the clinical response to these drugs based on CYP2D6 enzyme variation, however, inconsistent results exist between studies. Introduction: In 2021, 52.6 million people in the United States were treated for chronic pain, with 5 to 8 million American’s prescribed opioid medications to manage their pain. 1 Despite the analgesic benefits, opioids have many adverse side effects, including respiratory suppression, sedation, dizziness, nausea, vomiting, tolerance, and physical dependence. The WHO has developed an analgesic ladder to guide prescription opioids, which provides a step-wise approach to prescribing analgesic medications to cancer patients. 2 The idea is to start at the bottom of the ladder and climb a step at a time until analgesia is accomplished. Surgical patients might start at the top of the ladder and step down until analgesia is insufficient. This trial-and-error approach to find the most effective opioid for a patient can be time consuming, expose patients to unnecessary pain, and increase the risk of side effects. It is currently known that genetic variation contributes to both clinical effectiveness and risk for adverse events. Yet, this genetic variation has not been factored into the WHO analgesic ladder or how it is administered in medical practice. The objective of this work was to synthesize the current knowledge about how human genetic variation in the enzyme CYP2D6 affects the metabolism of some opioid drugs and propose a modified treatment algorithm for post-operative patients that may reduce the risk for poor and ultrarapid metabolizers. Introduction and Methods Methods: A literature search was conducted to understand the current knowledge of opioid medications and pharmacogenetic influence on their clinical effectiveness. PubMed, Google Scholar, the Clinical Pharmacogenetics Implementation Consortium (CPIC), and the Dutch Pharmacogenetics Working Group (DPWG) were utilized to understand the metabolism, mechanism of action and clinical response to opioid medications.

CYP2D6 Ultrarapid Metabolizers

CYP2D6 Poor Metabolizers

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Codeine has the lowest activity of the opioids commonly prescribed today. It is metabolized to the more potent metabolite, morphine, via oxidative-O-demethylation by CYP2D6. Oxycodone is fifteen times more potent than codeine and metabolized to the more potent oxymorphone, by CYP2D6. Oxymorphone is 10-30 times more potent than oxycodone. Hydrocodone is twelve times more potent than codeine and is metabolized to a more potent metabolite, hydromorphone, by CYP2D6. These activating modifications are opposed by an inactivating modification by CYP3A4. This liver enzyme causes an oxidative-N-demethylation that inactivates the opioids producing normorphine, nor-oxymorphone, or nor-hydromorphone depending on the starting opioid. There is also further glucuronidation of these compounds which further influences analgesia and adverse events.

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Figure 2: Ketamine metabolism

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Ketamine is a rapid acting NMDA receptor antagonist mainly used for the induction of anesthesia. 4 It is both water and lipid soluble with a large volume of distribution, making it rapid acting. 4 It also works on opioid receptors, monoaminergic receptors, muscarinic receptors, and voltage sensitive calcium ion channels. 4 It is metabolized to nor-ketamine via an N-demethylation reaction. 5 Ketamine and nor-ketamine can both be made water-soluble through hydroxylation. 5 Nor-ketamine can undergo further glucuronidation and dehydrogenation. 5 Limited evidence exists on the analgesic properties of the metabolites produced from these reactions. 6, 7

Figure 3: Suggested algorithm for CYP2D6 Poor Metabolizer

Figure 4: Suggested algorithm for CYP2D6 Ultra-rapid Metabolizer

Based on the current knowledge regarding mechanism of action, metabolism, properties and clinical response to each of the selected opioids in the original WHO algorithm, we propose the following analgesic treatment algorithm for CYP2D6 PM: oxycodone, hydrocodone/hydrocodone acetaminophen, morphine, hydromorphone, and ketamine. In CYP2D6 UM, we propose utilizing morphine, hydromorphone and ketamine due to the potential for adverse events these patients may have to oxycodone and hydrocodone.

Results

Conclusions

Genetic variation impacts the metabolism of opioids. Specifically, CYP2D6 is a major enzyme involved in the metabolism of oxycodone and hydrocodone to more potent analgesic compounds. Significant advancements have been made in understanding the clinical response to these drugs based on CYP2D6 enzyme variation, however, inconsistent results exist between studies. Further information regarding the clinical response of patients with genetic variation in CYP2D6 to analgesics will help direct more targeted and safe therapeutic management of pain.

Table 2. CPIC prescribing recommendations based on CYP2D6 enzyme activity. 6, 7

Table 1. Pre-developed pain management algorithm.

Thank you to Dr. Steven Enkemann, PhD for his time, patience and guidance during the creation of this project.

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2024 Research Recognition Day