Oscar A. Linares

Personalized Oxycodone Dosing: Using Pharmacogenetic Testing and Clinical Pharmacokinetics to Reduce Toxicity Risk and Increase Effectiveness

OBJECTIVEnTo develop a framework for integrating pharmacogenetics with clinical pharmacokinetics for personalized oxycodone dosing based on a patients CYP2D6 phenotype.nnnDESIGNnRandomized, crossover, double-blind, placebo-controlled. Subjects were genotyped as CYP2D6 ultra-rapid metabolizer, extensive metabolizer, or poor metabolizer phenotypes. Five subjects from each phenotype were randomly selected for inclusion in our study.nnnSETTINGnStudies were performed in silico.nnnSUBJECTSnThe subjects were male, age 26 years, height 181.2u2009cm, and weight 76.3u2009kg. They were healthy without comorbidities, and their medical examinations were normal.nnnMETHODSnThe trajectories of phenotype-specific plasma oxycodone concentration-time profiles were analyzed using weighted nonlinear least-squares regression with WinSAAM software. A global two-stage population-based model data analysis procedure was used to analyze the studies. Clinical pharmacokinetics were calculated using the R package cpk, eliminating the need to perform hand-calculations.nnnRESULTSnOur study shows how clinicians can reduce risk and increase effectiveness for oxycodone dosing by (1) determining the patients likely metabolic response through testing a patients CYP2D6 phenotype, and (2) calculating clinical pharmacokinetics specific to the patients CYP2D6 phenotype to design a personalized oxycodone dosing regimen.nnnCONCLUSIONSnPersonalized oxycodone dosing is a new tool for a clinician treating chronic pain patients requiring oxycodone. By expressing a patients CYP2D6 phenotype pharmacokinetically, a clinician (at least theoretically) can improve the safety and efficacy of oxycodone and decrease the risk for iatrogenically induced overdose or death. Pharmacokinomics provides a general framework for the integration of pharmacogenetics with clinical pharmacokinetics into clinical practice for gene-based prescribing.

CYP2D6 Phenotype-Specific Codeine Population Pharmacokinetics

ABSTRACT Codeines metabolic fate in the body is complex, and detailed quantitative knowledge of it, and that of its metabolites is lacking among prescribers. We aimed to develop a codeine pharmacokinetic pathway model for codeine and its metabolites that incorporates the effects of genetic polymorphisms. We studied the phenotype-specific time courses of plasma codeine, codeine-6-glucoronide, morphine, morphine-3-glucoronide, and morphine-6-glucoronide. A codeine pharmacokinetic pathway model accurately fit the time courses of plasma codeine and its metabolites. We used this model to build a population pharmacokinetic codeine pathway model. The population model indicated that about 10% of a codeine dose was converted to morphine in poor-metabolizer phenotype subjects. The model also showed that about 40% of a codeine dose was converted to morphine in EM subjects, and about 51% was converted to morphine in ultrarapid-metabolizers. The population model further indicated that only about 4% of MO formed from codeine was converted to morphine-6-glucoronide in poor-metabolizer phenotype subjects. The model also showed that about 39% of the MO formed from codeine was converted to morphine-6-glucoronide in extensive-metabolizer phenotypes, and about 58% was converted in ultrarapid-metabolizers. We conclude, a population pharmacokinetic codeine pathway model can be useful because beyond helping to achieve a quantitative understanding the codeine and MO pathways, the model can be used for simulation to answer questions about codeines pharmacogenetic-based disposition in the body. Our study suggests that pharmacogenetics for personalized dosing might be most effectively advanced by studying the interplay between pharmacogenetics, population pharmacokinetics, and clinical pharmacokinetics.

Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing.

Objectives:(1) To quantify hydrocodone (HC) and hydromorphone (HM) metabolite pharmacokinetics with pharmacogenetics in CYP2D6 ultra-rapid metabolizer (UM), extensive metabolizer (EM), and poor metabolizer (PM) metabolizer phenotypes. (2) To develop an HC phenotype-specific dosing strategy for HC that accounts for HM production using clinical pharmacokinetics integrated with pharmacogenetics for patient safety. Setting:In silico clinical trial simulation. Participants:Healthy white men and women without comorbidities or history of opioid, or any other drug or nutraceutical use, age 26.3±5.7 years (mean±SD; range, 19 to 36 y) and weight 71.9±16.8 kg (range, 50 to 108 kg). Main Outcome Measures:CYP2D6 phenotype-specific HC clinical pharmacokinetic parameter estimates and phenotype-specific percentages of HM formed from HC. Results:PMs had lower indices of HC disposition compared with UMs and EMs. Clearance was reduced by nearly 60% and the t1/2 was increased by about 68% compared with EMs. The canonical order for HC clearance was UM>EM>PM. HC elimination mainly by the liver, represented by ke, was reduced about 70% in PM. However, HC’s apparent Vd was not significantly different among UMs, EMs, and PM. The canonical order of predicted plasma HM concentrations was UM>EM>PM. For each of the CYP2D6 phenotypes, the mean predicted HM levels were within HM’s therapeutic range, which indicates HC has significant phenotype-dependent pro-drug effects. Conclusions:Our results demonstrate that pharmacogenetics afford clinicians an opportunity to individualize HC dosing, while adding enhanced opportunity to account for its conversion to HM in the body.

A New Model for Using Quantitative Urine Testing as a Diagnostic Tool for Oxycodone Treatment and Compliance

ABSTRACT We conducted a prospective, randomized, cross-sectional study to develop and validate a new model to predict oxycodone in urine that can be used to help evaluate whether patients are complying with their oxycodone dosing regimens. We studied 20 patients: eight black women, two white women, six black men, and four white men; ages 48 ± 10 years (mean ± SD); weight 97 ± 32 kg. Pain levels before treatment averaged 9.5 ± 0.9 out of 10. We prescribed oral oxycodone for each patient, tailoring the dosing regimen using clinical pharmacokinetics and measured the oxycodone concentration in each patients urine 10 to 14 days after starting the dosing regimen. For each patient, we predicted oxycodone in their urine using our model, checked the actual concentration, and compared predicted with actual concentrations. For 18 of 20 patients (90%), actual results fell within ±10% of our models prediction. One patient was 35% below the prediction; the other was 51% above. Our model accurately predicts oxycodone in urine (±10% for 90% of the patients). The model appears clinically useful for evaluating the results of a quantitative urine test, since it objectively discriminates between (1) a “normal” patient complying with their oxycodone dosing regimen, and (2) a patient who may require genetic testing to distinguish between unusual metabolism or abuse.

The CYP2D6 gene determines oxycodone’s phenotype-specific addictive potential: Implications for addiction prevention and treatment

We propose a hypothesis for predicting addictive potential of oral drugs, in general, and oxycodones addictive potential, in particular. We hypothesize that a patients CYP2D6 phenotype determines oxycodones addictive potential, in part, via genotype-specific regulation of its clearance; although, other possible modulators of oxycodones addiction potential exist. For example, brain CYPs related to phenotype could be involved. To pilot test our hypothesis, we used a mathematical model which postulates that oxycodones addictive potential is given by: LAP=E/(ka/ke), where LAP represents addictive potential, E represents euphoric potency, ka is the absorption rate constant of drug from the gastrointestinal tract, and ke is the systemic elimination rate constant of drug by all processes responsible for its removal from plasma. Using CYP2D6 phenotype-specific oxycodone pharmacokinetic parameter values derived from published data, our hypothesis predicted that the canonical order of oxycodones addictive potential was UM>EM>IM>PM, with corresponding LAP values of 0.24, 0.21, 0.17, and 0.15 respectively. Our hypothesis about oxycodones addictive potential may provide a unifying approach useful for both personalized medicine dosing and predicting addictive potential of oral drugs in humans, since it is based on both oxycodones pharmacogenetics and pharmacokinetics.

In silico ordinary differential equation/partial differential equation hemodialysis model estimates methadone removal during dialysis.

Background There is a need to have a model to study methadone’s losses during hemodialysis to provide informed methadone dose recommendations for the practitioner. Aim To build a one-dimensional (1-D), hollow-fiber geometry, ordinary differential equation (ODE) and partial differential equation (PDE) countercurrent hemodialyzer model (ODE/PDE model). Methodology We conducted a cross-sectional study in silico that evaluated eleven hemodialysis patients. Patients received a ceiling dose of methadone hydrochloride 30 mg/day. Outcome measures included: the total amount of methadone removed during dialysis; methadone’s overall intradialytic mass transfer rate coefficient, km; and, methadone’s removal rate, jME. Each metric was measured at dialysate flow rates of 250 mL/min and 800 mL/min. Results The ODE/PDE model revealed a significant increase in the change of methadone’s mass transfer with increased dialysate flow rate, %Δkm=18.56, P=0.02, N=11. The total amount of methadone mass transferred across the dialyzer membrane with high dialysate flow rate significantly increased (0.042±0.016 versus 0.052±0.019 mg/kg, P=0.02, N=11). This was accompanied by a small significant increase in methadone’s mass transfer rate (0.113±0.002 versus 0.014±0.002 mg/kg/h, P=0.02, N=11). The ODE/PDE model accurately predicted methadone’s removal during dialysis. The absolute value of the prediction errors for methadone’s extraction and throughput were less than 2%. Conclusion ODE/PDE modeling of methadone’s hemodialysis is a new approach to study methadone’s removal, in particular, and opioid removal, in general, in patients with end-stage renal disease on hemodialysis. ODE/PDE modeling accurately quantified the fundamental phenomena of methadone’s mass transfer during hemodialysis. This methodology may lead to development of optimally designed intradialytic opioid treatment protocols, and allow dynamic monitoring of outflow plasma opioid concentrations for model predictive control during dialysis in humans.

Oxycodone recycling: a novel hypothesis of opioid tolerance development in humans.

We hypothesize that oxycodone (OC) recycling promotes sustained synaptic OC content, which prolongs OCs exposure to local μ-opioid receptors (μORs). In that way, OC recycling gives rise to OC tolerance in humans. To pilot test our hypothesis, we developed a whole-body OC mass transport tolerance recovery model. The model derived quantifiable measure of tolerance is TΩ. TΩ estimates OCs tolerance recovery in days; It is defined as the rate of recovery of OCs pharmacologic response after OC is stopped. We studied a random sample of five opioid intolerant healthy male subjects with no history of opioid or illicit drug use, or comorbidities in silico. Subjects were age 24.5 ± 2.3 yr (all values mean ± SD), weight 93 ± 20 kg, and CYP2D6 EM phenotype. Each subject was studied under two experimental conditions: (1) administration of a single oral dose of OC 12 ± 7 mg; and, after complete washout of OC from the intravascular pool, (2) administration of repetitive oral OC doses every 4h for 5 half-lives (t1/2 = 4.5h)-after which time steady-state was assumed. Repetitive OC dose TΩ fell 61% compared to single OC dose TΩ (5.2 ± 1.1 vs. 3.5 ± 0.7 days, p = 0.001). The fall in TΩ was associated with a significant 3-fold increase in extravascular OC content, which was accompanied by 2-fold increase in OC spillover from the extravascular pool, into the intravascular pool. Thus, the model predicted that a single dose of orally administered OC could give rise to tolerance. This is consistent with the widely held view of acute opioid tolerance. In addition, the dynamic changes accompanying repetitive OC dosing suggested that local unbound OC gave rise to both higher extravascular OC content and increased OC spillover. This reflects that OC stimulated endocytosis of μORs was accompanied by a reduction in the availability OC responsive neuroeffector cell surface μOR binding sites. We conclude that our hypothesis extends current concepts of opioid tolerance development to include OC recycling. OC recycling is a novel hypothesis of OC tolerance development in humans.

The Linares Addictive Potential Model

The Salerian Addictive Potential (SAP) hypothesis indicates that addictive potential may be calculated as A = E / Tmaxufffbt1/ 2 , where A is addictive potency, E euphoric potency, Tmax (hr) is the time to reach peak plasma concentration, and t� (hr) is the plasma elimination half-life. However, this approach is inconsistent with first-order linear pharmacokinetics. The units of the denominator of the equation are units of acceleration (hr 2 ), not speed (the first derivative). Therefore, the present contribution presents a minimal-model hypothesis for quantifying a drugs addictive potential. This model is superior to the SAP model because it is the simplest model, with the minimum number of parameters and assumptions, and it decreases variance through less loss of information.

Organ-specific microcirculatory mass transport of oxycodone in humans: clinical implications for therapeutic use.

Objectives:To begin to address the problem of heterogeneity of distribution of oxycodone (OC) in humans, we developed an organ-specific microcirculatory capillary-tissue exchange 2-compartment model for studying regional OC mass transport. Materials and Methods:The model was developed in silico. It quantifies OC’s organ-specific mass transport rates, clearances and recycling, and it considers the effects of blood flow on OC’s convective and diffusive transport. Results:What is new is the finding that OC undergoes local recycling at the level of organ-specific capillary-tissue exchange units in humans. Results indicate recycled OC occurs in sufficient amounts to function as a reusable source of circulating OC; which has important implications for OC dosing. Results show the brain, which is central to OC effects only receives about 8% of OC delivered to all organs via the microcirculation. This suggests that differential regulation of receptor binding, trafficking, internalization, or desensitization in the brain likely plays a dominant role in OC’s central analgesic effects. Discussion:Organ-specific OC mass transport kinetics provide new information for OC dosing in pain management. The model promotes patient safety in opioid prescribing because it allows predictions to be made about the relative contribution that OC recycling makes to circulating OC levels. The model indicates that pharmacologic modulation of the microcirculation may give way to site-specific delivery of opioids in the future. Our study demonstrates that translation of bench in silico research data into clinical practice, although still challenging, is feasible and can assist in OC dose regimen design for patient safety.

Feasibility and Utility of the Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing.

To the Editor: We read with interest the article by Linares et al,1 entitled “Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing,” in The Clinical Journal of Pain. They aimed at developing a hydrocodone (HC) phenotypespecific dosing strategy for HC that accounts for hydromorphone (HM) production using clinical pharmacokinetics integrated with pharmacogenetics for patient safety. They found that, for each of the CYP2D6 phenotypes, the mean predicted HM levels were within HM’s therapeutic range, which indicates HC has significant phenotype-dependent prodrug effects. Linares et al1 conclude that their results demonstrate that pharmacogenetics afford clinicians an opportunity to individualize HC dosing, while adding enhanced opportunity to account for its conversion to HM in the body. We commend Linares and colleagues for the assiduous effort in conducting this search and preparing this interesting manuscript. The study attempts to help clinicians to individualize HC dosing by accounting for conversion of HC to HM when prescribing hydrocodone as an analgesic. This insightful work is of interest scientifically, especially in light of the burgeoning field of pharmacogenomics, but we feel the utility of this approach is rather limited in dosing HC for the therapeutic control of pain in the clinic. Two prior studies serve as the foundation for the work of Linares and colleagues. Stauble et al examined whether CYP2D6 genotype and serum HM levels account for some of the variability in pain relief seen with HC in a cohort of women post-Cesarean section. They found that HM is generated at substantially different rates, dependent on CYP2D6 genotype. Pain relief correlated with plasma concentrations of HM, and not with HC, thus implying that pain relief varies with CYP2D6 genotype.2 Boswell et al3 evaluated the relationship of analgesia, side effects, total HC consumption, quantitative serum HC and HM concentrations, and single nucleotide polymorphisms of the human opioid mu-receptor (OPRM1) A118G single nucleotide polymorphism in postoperative patients following Cesarean section. They found a correlation between pain relief and total HC dose in patients homozygous for the 118A allele (AA) of the OPRM1 gene, but not in patients with the 118G allele (AG/GG). However, pain relief in 118A patients did not correlate with serum HC concentrations, but rather with serum HM levels. Boswell et al3 believe their finding suggest that pain relief with HC may be related primarily to HM. However, like other centrally acting drugs, opioids rely on the free drug concentration in the brain tissue (biophase)4; therefore, plasma drug levels of opioids are not usually correlated with analgesia. This may also be due to differences in other variables, such as transport of the drug across the blood brain barrier, the ability to bind and to activate opioid receptors, possible variation in opioid receptor expression, and the individual’s inherent biological system in modifying pain perception.5 All this is further confounded by highly variable, in terms of onset and degree, tolerance phenomena, seen in the clinic. Therefore, the findings of the associations of plasma level of HM with analgesia should be considered an interesting correlation, rather than evidence to support the concept of “phenotypical prodrug.” HC is simply an opioid with a more active metabolite contributing to analgesia, just like morphine with a more active morphine-6-glucuronide, oxycodone with a more active oxymorphone. Prodrugs, by definition, are chemically modified versions of the pharmacologically active agent that must undergo transformation in vivo to release the active drug.6 The lack of association of plasma HC level with analgesia does not rule out its role in opioid analgesia, as many other opioids behave in a similar manner. The association of plasma HM level with analgesia does not prove HC is a prodrug either. Susce and colleagues reported an 85year-old women, identified as CYP2D6 poor metabolizer, who could not tolerate oxycodone and tramadol (as needed), responded better with HC.7 We have seen many cases of patients with known CYP2D6 poor metabolizer obtaining significant pain relief with using HC in our practice. Both HC and norhydrocodone participate in opioid analgesia. Navani and Yoburn8 showed that in mice, following subcutaneous administration, norhydrocodone was B70-fold less potent, and HM was B5.4-fold more potent than hydrocodone in producing analgesia. Further, following intrathecal administration, norhydrocodone produced a shallow analgesia dose-response curve and maximal effect of 15% to 45%, whereas HC and HM produced dose-dependent analgesia. Intrathecal HM was B174-fold more potent than intrathecal hydrocodone. Analgesia induced by the 3 drugs following subcutaneous, intrathecal, and intracerebroventricular administration was antagonized by subcutaneous naltrexone, confirming that it is opioid receptor-mediated.8 Navani and Yoburn8 conclude that both hydrocodone and norhydrocodone participate in opioid analgesia. In this regard, the statements made by Linares et al1 “HC is converted to HM by CYP2D6 for analgesia” and “Norhydrocodone is an inactive metabolite of HC” may not be accurate. Lastly, if the assumption that “HC is a prodrug in human” were true, then we would need to ask ourselves whether or not we really need to use HC at all. Are we better off to simply use HM, the active drug instead of HC, as HM is widely available and we can avoid all the therapeutic uncertainties associated with the potential troubles and difficulties of using a “prodrug,” as well as the associated requirement for genetic testing for CYP2D6 phenotypes, without having to go through a myriad of pharmacokinetic calculations in the end to determine the HC dose to obtain analgesia in the clinic?