CYP2D6 Phenotype-Specific Codeine Population Pharmacokinetics

Population Pharmacokinetic Quantification of CYP2D6 Activity in Codeine Metabolism in Ambulatory Surgical Patients for Model-Informed Precision Dosing

BACKGROUND AND OBJECTIVE

Codeine metabolism in humans is complex due to the involvement of multiple cytochrome P450 (CYP) enzymes, and has a strong genetic underpinning, which determines the levels of relevant CYP450 enzyme expression in vivo. Polymorphic CYP2D6 metabolises codeine to morphine via O-demethylation, while a strong correlation between CYP2D6 phenotype and opioidergic adverse effects of codeine is well documented. The aim of this study was to quantify the effect of CYP2D6 genotype on the biotransformation of codeine.

METHODS

We conducted a prospective clinical trial with 1000 patients, during which ambulatory patients were administered 60 mg of codeine preoperatively and the association between CYP2D6 activity and morphine exposure across various CYP2D6 genotypes was quantified using a population pharmacokinetic model. Plasma concentration data for codeine and its primary metabolites were obtained from 997 patients and CYP2D6 genotype was screened for study subjects, and respective sums of activity scores assigned for each CYP2D6 allele were used as covariates in model development.

RESULTS

Our final model predicts the disposition of codeine and the formation of morphine, codeine-6-glucuronide and morphine-3-glucuronide adequately while accounting for variability in morphine exposure on the basis of CYP2D6 genotype. In agreement with previous results, patients with decreased function alleles (CYP2D6*10 and *41) showed varying levels of decrease in CYP2D6 activity that were inconsistent with increasing activity scores. Model simulations demonstrate that morphine concentrations in ultrarapid CYP2D6 metabolisers reach systemic concentrations that can potentially cause respiratory depression (over 9.1 ng/mL), and have 218% higher exposure (19 versus 8.7 µg · h/L, p < 0.001) to morphine than normal metabolisers. Similarly, poor and intermediate metabolisers had significantly reduced morphine exposure (1.0 and 3.7 versus 8.7 µg · h/L, p < 0.001) as compared with normal metabolisers.

CONCLUSIONS

Our final model leads the way in implementing model-informed precision dosing in codeine therapy and identifies the use of genetic testing as an integral component in the effort to implement rational pharmacotherapy with codeine.

Physiologically based pharmacokinetic modeling to predict the pharmacokinetics of codeine in different CYP2D6 phenotypes

Objectives

Codeine, a prodrug used as an opioid agonist, is metabolized to the active product morphine by CYP2D6. This study aimed to establish physiologically based pharmacokinetic (PBPK) models of codeine and morphine and explore the influence of CYP2D6 genetic polymorphisms on the pharmacokinetics of codeine and morphine.

Methods

An initial PBPK modeling of codeine in healthy adults was established using PK-Sim® software and subsequently extrapolated to CYP2D6 phenotype-related PBPK modeling based on the turnover frequency (Kcat) of CYP2D6 for different phenotype populations (UM, EM, IM, and PM). The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between the predicted and observed values of the pharmacokinetic parameters to evaluate the accuracy of PBPK modeling. The validated models were then used to support dose safety for different CYP2D6 phenotypes.

Results

The developed and validated CYP2D6 phenotype-related PBPK model successfully predicted codeine and morphine dispositions in different CYP2D6 phenotypes. Compared with EMs, the predicted AUC0-∞ value of morphine was 98.6% lower in PMs, 60.84% lower in IMs, and 73.43% higher in UMs. Morphine plasma exposure in IMs administered 80 mg of codeine was roughly comparable to that in EMs administered 30 mg of codeine. CYP2D6 UMs may start dose titration to achieve an optimal individual regimen and avoid a single dose of over 20 mg. Codeine should not be used in PMs for pain relief, considering its insufficient efficacy.

Conclusion

PBPK modeling can be applied to explore the dosing safety of codeine and can be helpful in predicting the effect of CYP2D6 genetic polymorphisms on drug-drug interactions (DDIs) with codeine in the future.

Managing pain associated with dermatologic procedures

Dermatologists are faced with an aging population, accompanied by an increase in the incidence of skin cancer, especially nonmelanoma skin cancer. With this increase in cutaneous malignancy comes an increase in the number of dermatologic procedures being performed. A common concern of dermatologists and dermatology patients is the attenuation and management of pain associated with dermatologic procedures. Fortunately, there are several techniques that may be used in the preoperative, intraoperative, and postoperative periods to manage patient anxiety, minimize injection pain, and safely and effectively treat postoperative pain.

Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure

Guidance regarding the effect of codeine and its metabolites on foetal development is limited by small studies and inconsistent findings. The primary objective was to use physiologically based pharmacokinetic modelling to investigate the impact of gestational stage and maternal CYP2D6 phenotype on foetal morphine exposure following codeine administration. Full body physiologically based pharmacokinetic models were developed and verified for codeine and morphine using Simcyp (version 19.1). The impact of gestational age and maternal CYP2D6 phenotype on foetal and maternal morphine and codeine exposure following oral codeine administration was modelled in a cohort of 250 pregnant females and foetuses at gestational weeks 0 (mothers only), 6, 12, 24 and 36. Consistent with the known effect on codeine metabolism, a clinically meaningful (> 1.65-fold) increase in foetal morphine AUC was observed in the CYP2D6 UM phenotype cohort compared to the CYP2D6 EM and PM phenotype cohorts. The mean (95% CI) foetal morphine AUC in the CYP2D6 UM cohort of 0.988 (0.902 to 1.073) ng/mL.h was 1.8-fold higher than the CYP2D6 EM cohort of 0.546 (0.492 to 0.600) ng/mL.h (p < 0.001). Despite a 2.8-fold increase in maternal CYP2D6 protein abundance between gestational weeks 6 and 36, the mean foetal morphine AUC in the CYP2D6 EM and UM phenotype cohorts reduced by 1.55- and 1.75-fold, respectively, over this period. Maternal CYP2D6 phenotype is a significant determinant of foetal morphine AUC. Simulations suggest that the greatest risk with respect to foetal morphine exposure is during the first trimester of pregnancy, particularly in CYP2D6 UM phenotype mothers.

Genomics and electronic health record systems

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

Genomics Testing and Personalized Medicine in the Preoperative Setting

Pharmacogenomics (PGx) is the study of how individuals' personal genotypes may affect their responses to various pharmacologic agents. The application of PGx principles in perioperative medicine is fairly novel. Challenges in executing PGx programs into health care systems include physician buy-in and integration into usual clinical workflow, including the electronic health record. This article discusses the current evidence highlighting the potential of PGx with various drug categories (including opioids, nonopioid analgesics, sedatives, β-blockers, antiemetics, and anticoagulants) used in the perioperative process and the challenges of integrating PGx into a health care system and relevant workflows.

Update of the Scientific Opinion on opium alkaloids in poppy seeds

Poppy seeds are obtained from the opium poppy (Papaver somniferum L.). They are used as food and to produce edible oil. The opium poppy plant contains narcotic alkaloids such as morphine and codeine. Poppy seeds do not contain the opium alkaloids, but can become contaminated with alkaloids as a result of pest damage and during harvesting. The European Commission asked EFSA to provide an update of the Scientific Opinion on opium alkaloids in poppy seeds. The assessment is based on data on morphine, codeine, thebaine, oripavine, noscapine and papaverine in poppy seed samples. The CONTAM Panel confirms the acute reference dose (ARfD) of 10 μg morphine/kg body weight (bw) and concluded that the concentration of codeine in the poppy seed samples should be taken into account by converting codeine to morphine equivalents, using a factor of 0.2. The ARfD is therefore a group ARfD for morphine and codeine, expressed in morphine equivalents. Mean and high levels of dietary exposure to morphine equivalents from poppy seeds considered to have high levels of opium alkaloids (i.e. poppy seeds from varieties primarily grown for pharmaceutical use) exceed the ARfD in most age groups. For poppy seeds considered to have relatively low concentrations of opium alkaloids (i.e. primarily varieties for food use), some exceedance of the ARfD is also seen at high levels of dietary exposure in most surveys. For noscapine and papaverine, the available data do not allow making a hazard characterisation. However, comparison of the dietary exposure to the recommended therapeutical doses does not suggest a health concern for these alkaloids. For thebaine and oripavine, no risk characterisation was done due to insufficient data. However, for thebaine, limited evidence indicates a higher acute lethality than for morphine and the estimated exposure could present a health risk.

Endogenous Opiates and Behavior: 2016

This paper is the thirty-ninth consecutive installment of the annual review of research concerning the endogenous opioid system. It summarizes papers published during 2016 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides, opioid receptors, opioid agonists and opioid antagonists. The particular topics that continue to be covered include the molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors related to behavior, and the roles of these opioid peptides and receptors in pain and analgesia, stress and social status, tolerance and dependence, learning and memory, eating and drinking, drug abuse and alcohol, sexual activity and hormones, pregnancy, development and endocrinology, mental illness and mood, seizures and neurologic disorders, electrical-related activity and neurophysiology, general activity and locomotion, gastrointestinal, renal and hepatic functions, cardiovascular responses, respiration and thermoregulation, and immunological responses.

Pharmacogenomics in pain treatment

The experience of chronic pain is one of the commonest reasons for seeking medical attention, being a major issue in clinical practice. While pain is a universal experience, only a small proportion of people who felt pain develop pain syndromes. In addition, painkillers are associated with wide inter-individual variability in the analgesic response. This may be partly explained by the presence of single nucleotide polymorphisms in genes encoding molecular entities involved in pharmacodynamics and pharmacokinetics. However, uptake of this information has been slow due in large part to the lack of robust evidences demonstrating clinical utility. Furthermore, novel therapies, including targeting of epigenetic changes and gene therapy-based approaches are further broadening future options for the treatment of chronic pain. The aim of this article is to review the evidences behind pharmacogenetics (PGx) to individualize therapy (boosting the efficacy and minimizing potential toxicity) and genes implicated in pain medicine, in two parts: (i) genetic variability with pain sensitivity and analgesic response; and (ii) pharmacological concepts applied on PGx.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

Pharmacogenomics in Anesthesia

A significant number of commonly administered medications in anesthesia show wide clinical interpatient variability. Some of these include neuromuscular blockers, opioids, local anesthetics, and inhalation anesthetics. Individual genetic makeup may account for and predict cardiovascular outcomes after cardiac surgery. These interactions can manifest at any point in the perioperative period and may also only affect a specific system. A better understanding of pharmacogenomics will allow for more individually tailored anesthetics and may ultimately lead to better outcomes, decreased hospital stays, and improved patient satisfaction.

Pharmacogenomics in Pain Management

There is interpatient variability to analgesic administration. Much can be traced to pharmacogenomics variations between individuals. Certain ethnicities are more prone to reduced function of CYP2D6. Weak opioids are subject to interpatient variation based on their CYP2D6 type. Strong opioids have variations based on their transport and individual metabolism. Several cytochrome enzymes have been found to be involved with ketamine but there is no strong evidence of individual polymorphisms manifesting in clinical outcomes. Nonsteroidal anti-inflammatory drugs have adverse outcomes that certain CYP variants are more prone toward. There are now recommendations for dosing based on specific genomic makeup.

CYP450 genotype and pharmacogenetic association studies: a critical appraisal

Despite strong pharmacological support, association studies using genotype-predicted phenotype as a variable have yielded conflicting or inconclusive evidence to promote personalized pharmacotherapy. Unless the patient is a genotypic poor metabolizer, imputation of patient's metabolic capacity (or metabolic phenotype), a major factor in drug exposure-related clinical response, is a complex and highly challenging task because of limited number of alleles interrogated, population-specific differences in allele frequencies, allele-specific substrate-selectivity and importantly, phenoconversion mediated by co-medications and inflammatory co-morbidities that modulate the functional activity of drug metabolizing enzymes. Furthermore, metabolic phenotype and clinical outcomes are not binary functions; there is large intragenotypic and intraindividual variability. Therefore, the ability of association studies to identify relationships between genotype and clinical outcomes can be greatly enhanced by determining phenotype measures of study participants and/or by therapeutic drug monitoring to correlate drug concentrations with genotype and actual metabolic phenotype. To facilitate improved analysis and reporting of association studies, we propose acronyms with the prefixes ‘g’ (genotype-predicted phenotype) and ‘m’ (measured metabolic phenotype) to better describe this important variable of the study subjects. Inclusion of actually measured metabolic phenotype, and when appropriate therapeutic drug monitoring, promises to reveal relationships that may not be detected by using genotype alone as the variable.