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Pain Medicine 2014; 15: 723–725 Wiley Periodicals, Inc.
EDITORIALS Personalized Oxycodone Dosing: Using Pharmacogenetic Testing and Clinical Pharmacokinetics to Reduce Toxicity Risk and Increase Effectiveness
Disclosures: Dr. Fudin is on the speakers’ bureau for Millennium Laboratories, Inc. He is a consultant to Practical Pain Management in the development of the online Opioid Calculator. Dr. Atkinson has no conflicts to disclose. This commentary is the sole opinion of the authors and does not reflect the opinion of employers, employee affiliates, and/or pharmaceutical companies listed. It was not prepared as part of Drs. Fudin and Atkinson’s official government duties as clinical pharmacy specialists. Pharmacogenomics is an emerging field that brings clinical relevance to genetic markers by individualizing drug selection and/or dose [1,2]. At the heart of this field is the concept that specific genes may hold the key to success or failure for critical therapies. Treatments for some cancer types are already guided by individuals’ expression of specific genes [3–5]. For human immunodeficiency virus (HIV), pharmacogenetic studies, specifically “genotyping” studies, are utilized to select more effective therapy combinations or avoid potentially life-threatening adverse reactions and maximize efficacy [6–8]. Likewise, pharmacogenomic testing for crucial metabolic pathways should enable prediction of clinical drug response, especially when targeted binding sites or tissues are being considered [9,10]. One of the most appealing aspects of pharmacogenomic testing for metabolic pathways is practical utilization of this information clinically and physiologically for ultimate application and targeted patientspecific treatment. Identification of affected pathways could aid in clinical decision-making, drug selection, appropriate individualized calculated dosing, and weighing of wide-ranging treatment risks vs overall clinical outcomes, all of which could become essential portions of a patient’s lifetime genetic profile within his or her medical record. Linares and colleagues provide an excellent overview of the application of clinical pharmacokinetics and its usefulness in calculating patient-specific values to target a useful therapeutic range for oxycodone [11]. In clinical practice, skilled practitioners currently utilize patient-
specific, albeit nongenetic, pharmacokinetics to effectively dose antibiotics (vancomycin, aminoglycosides) where peak and trough serum values are closely correlated with clinical outcomes. The mathematical formulas may seem complex and unfamiliar, but they are invaluable to pharmaceutical industry researchers, who rely upon clinical pharmacokinetics to identify appropriate dose-response during early clinical trials. Such mathematical maneuvers are particularly useful to avert drug toxicity and ascertain predictable therapeutic ranges devoid of pharmacogenomic considerations—we have seen the practical use of such calculations by clinical pharmacists over many years with common drugs, including digoxin, several antipsychotics, lithium, various aminoglycosides, phenytoin, and even theophylline, to name a few [12]. The authors clearly selected cytochrome 450 2D6 for its well-documented polymorphic differences, yet they acknowledge that 2D6 is only a minor part of the metabolic pathway involved in oxycodone metabolism [11]. Predictably, the expressed genetic phenotype (extensive metabolizer, poor metabolizer, or ultrametabolizer) had little effect on dosing regimen, so the effect size was increased by adjusting the target serum concentration within the therapeutic range [11]. If the same therapeutic target is utilized (33 μg/L), the results are 5.6 mg for poor metabolizers (5 mg q.4h.), 8 mg for extensive metabolizers (7.5 mg q.4h.), and 9 mg for ultrametabolizers (10 mg q.4h.). For some practitioners, it may be difficult to comprehend the practical clinical relevance, yet this is mostly reflective of dose adjustment based upon a minor metabolic pathway [11]. The result would have been far different if Linares and colleagues had studied codeine or tramadol, both of which are prodrugs dependent on 2D6 metabolism for activation and eventual analgesic (or toxic) effect [13–15]. A predictable therapeutic range is not available for opioids. This is particularly true for chronic pain patients, for whom tolerance and cross-tolerance must be considered when dosing chronically and supplementing for breakthrough or incidental pain in both the ambulatory and acute care settings. No experienced pain practitioner would believe that oxycodone 5–10 mg dosed every 4 hours would 723
Fudin and Atkinson adequately treat every patient’s pain regardless of 2D6 phenotype. This is in part due to polymorphic differences in opioid-receptor makeup among various patients [16,17]. The authors’ listed oxycodone serum concentration (20–50 μg/L) originates from toxicology data collected by Schulz et al., who acknowledge these values are meant only as orientation because reported values are a single serum concentration without perspective of time between drug intake and blood sample, dosing interval, accumulation, tolerance, or involvement of concomitant diseases [11,18]. Therefore, therapeutic ranges listed for each drug are meant only as a guide, not as targets for dosing. Current practical obstacles for implementation of pharmacogenomic testing and commensurate therapeutic drug monitoring (TDM) include cost, laboratory turn-around time, clinician availability and calculation time (with or without electronic computing aids), and lack of expertise in interpretation among the vast majority of clinicians. Deviations in drug metabolism can have serious consequences or enormous value as part of a patient’s clinical profile. Likewise, TDM is significantly helpful in the evaluation of therapeutic response, drug interactions, and compliance. Extensive research is necessary, however, to apply pharmacogenomic testing and TDM to target a specific therapeutic range, because population-based studies documenting more than a single serum level do not exist. Moreover, as this is an area where incorrect calculations could result in patient harm or even death, it is necessary for studies to include chronic pain patients, not just healthy volunteers, and measure dose-response with frequent TDM. In short, we agree that pharmacogenomics and TDM combined with clinical pharmacokinetics could have widespread applicability to improve patient outcomes, but it may be premature to coin the term “pharmacokinomics” for personalized oxycodone dosing without further evidence across a wide polymorphic sample. Nevertheless, Linares and colleagues [11] offer a very exciting glimpse into a future where “pharmacokinomics” could or should be considered—one where biotechnology and medicine have become intertwined with improved access to electronic records and where necessary computational tools have become embedded ubiquitously as part of the charting process. The authors deserve much praise for their innovative foresight into the future of medicine, particularly its integration of medicine, clinical pharmacy, and pharmacokinetics. Such exciting collaboration across scientific, mathematical, pharmaceutical, and medical disciplines exemplifies the principle that alterum alterius auxilio eget, “each needs the help of the other,” for the advancement of medication safety and improved patient outcomes.
JEFFREY FUDIN, BS, PharmD, FCCP*†‡ TIMOTHY J. ATKINSON, PharmD§ *Adjunct Assistant Professor of Pharmacy Practice School of Pharmacy University of Connecticut 724
Storrs, Connecticut, USA Adjunct Associate Professor of Pharmacy Practice & Pain Management Albany College of Pharmacy & Health Sciences Albany, New York, USA ‡ Clinical Pharmacy Specialist and Director PGY2 Pain & Palliative Care Pharmacy Residency Stratton VA Medical Center Albany, New York, USA § Resident PGY2 Pain & Palliative Care Pharmacy Residency Stratton VA Medical Center Albany, New York, USA
†
References 1 Jonas D, Wines R. Pharmacogenomic testing and the prospect of individualized treatment. N C Med J 2013;74(6):485–93. 2 Polk B, Becker M. Pharmacogenomics in childhood rheumatic disorders: A foundation for future individualized therapy. Discov Med 2013;16(90):267–75. 3 Liu X, Cheng D, Kuang Q, et al. Association between UGT1A1*28 polymorphisms and clinical outcomes of irinotecan-based chemotherapies in colorectal cancer: A meta-analysis in Caucasians. PLoS One 2013;8(3):e58489. 4 Chen J, Zhao Q, Shi G, Wang L. XRCC1 Arg399Gln and clinical outcome of platinum-based treatment for advanced non-small cell lung cancer: A meta-analysis in 17 studies. J Zhejiang Univ Sci B 2012;13(11):875– 83. 5 Gamazon E, Lamba J, Pounds S, et al. Comprehensive genetic analysis of cytarabine sensitivity in a cellbased model identifies polymorphisms associated with outcome in AML patients. Blood 2013;121(21): 4366–76. 6 Molto J, Xinarianos G, Miranda C, et al. Simultaneous pharmacogenetics-based population pharmacokinetic analysis of darunavir and ritonavir in HIV-infected patients. Clin Pharmacokinet 2013;52(7):543–53. 7 Kagan R, Johnson E, Siaw W, et al. A genotypic test for HIV-1 tropism combining Sanger sequencing with ultradeep sequencing predicts virologic response in treatment-experienced patients. PLoS ONE 2012; 7(9):e46334. 8 Carlyn CJ, Baltch AL, Smith RP, et al. HIV genotyping for optimizing therapy. Presented at the Infectious Disease Society of America 38th Annual Meeting, New Orleans, Louisiana, September 7–10, 2000. 9 Wang W, Wang N, Wang J, Wang Z, Wu R. Delivering systems pharmacogenomics towards precision medicine through mathematics. Adv Drug Deliv Rev 2013;65(7):905–11.
Editorial 10 Arab-Alameddine M, Decosterd L, Buclin T, Telenti A, Csajka C. Antiretroviral drug toxicity in relation to pharmacokinetics, metabolic profile and pharmacogenetics. Expert Opin Drug Metab Toxicol 2011;7(5): 609–22. 11 Linares O, Daly D, Linares A, Setefanovski D, Boston R. Personalized oxycodone dosing: Using pharmacogenetic testing and clinical pharmacokinetics to reduce toxicity risk and increase effectiveness. Pain Med 2014. doi:10.1111/pme.12380. [Epub ahead of print]. 12 DiPiro J, Spruill W, Wade W, Blouin R, Pruemer J. Concepts in Clinical Pharmacokinetics, 4th edition. Besthesda, MD: American Society of Health-System Pharmacists; 2005. 13 Ciszkowski C, Madadi P, Phillips M, Lauwers A, Koren G. Codeine, ultrarapid metabolism genotype, and postoperative death. N Engl J Med 2009;361(8): 827–8.
14 Madadi P, Ross C, Hayden M, et al. Pharmacogenetics of neonatal opioid toxicity following maternal use of codeine during breastfeeding: A case–control study. Clin Pharmacol Ther 2009;85(1):31–5. 15 Gan S, Ismail R, Wan Adnan W, Zulmi W. Impact of CYP2D6 genetic polymorphism on tramadol pharmacokinetics and pharmacodynamics. Mol Diagn Ther 2007;11(3):171–81. 16 Pasternak G. Opioids and their receptors: Are we there yet? Neuropharmacology 2014;76:198– 203. 17 Pasternak G, Pan Y. Mu opioids and their receptors: Evolution of a concept. Pharmacol Rev 2013;65(4): 1257–317. 18 Schulz M, Schmoldt A. Therapeutic and toxic blood concentrations of more than 800 drugs and other xenobiotics. Pharmazie 2007;58:447–74.
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Pain Medicine 2014; 15: 723–725 Wiley Periodicals, Inc.
EDITORIALS Personalized Oxycodone Dosing: Using Pharmacogenetic Testing and Clinical Pharmacokinetics to Reduce Toxicity Risk and Increase Effectiveness
Disclosures: Dr. Fudin is on the speakers’ bureau for Millennium Laboratories, Inc. He is a consultant to Practical Pain Management in the development of the online Opioid Calculator. Dr. Atkinson has no conflicts to disclose. This commentary is the sole opinion of the authors and does not reflect the opinion of employers, employee affiliates, and/or pharmaceutical companies listed. It was not prepared as part of Drs. Fudin and Atkinson’s official government duties as clinical pharmacy specialists. Pharmacogenomics is an emerging field that brings clinical relevance to genetic markers by individualizing drug selection and/or dose [1,2]. At the heart of this field is the concept that specific genes may hold the key to success or failure for critical therapies. Treatments for some cancer types are already guided by individuals’ expression of specific genes [3–5]. For human immunodeficiency virus (HIV), pharmacogenetic studies, specifically “genotyping” studies, are utilized to select more effective therapy combinations or avoid potentially life-threatening adverse reactions and maximize efficacy [6–8]. Likewise, pharmacogenomic testing for crucial metabolic pathways should enable prediction of clinical drug response, especially when targeted binding sites or tissues are being considered [9,10]. One of the most appealing aspects of pharmacogenomic testing for metabolic pathways is practical utilization of this information clinically and physiologically for ultimate application and targeted patientspecific treatment. Identification of affected pathways could aid in clinical decision-making, drug selection, appropriate individualized calculated dosing, and weighing of wide-ranging treatment risks vs overall clinical outcomes, all of which could become essential portions of a patient’s lifetime genetic profile within his or her medical record. Linares and colleagues provide an excellent overview of the application of clinical pharmacokinetics and its usefulness in calculating patient-specific values to target a useful therapeutic range for oxycodone [11]. In clinical practice, skilled practitioners currently utilize patient-
specific, albeit nongenetic, pharmacokinetics to effectively dose antibiotics (vancomycin, aminoglycosides) where peak and trough serum values are closely correlated with clinical outcomes. The mathematical formulas may seem complex and unfamiliar, but they are invaluable to pharmaceutical industry researchers, who rely upon clinical pharmacokinetics to identify appropriate dose-response during early clinical trials. Such mathematical maneuvers are particularly useful to avert drug toxicity and ascertain predictable therapeutic ranges devoid of pharmacogenomic considerations—we have seen the practical use of such calculations by clinical pharmacists over many years with common drugs, including digoxin, several antipsychotics, lithium, various aminoglycosides, phenytoin, and even theophylline, to name a few [12]. The authors clearly selected cytochrome 450 2D6 for its well-documented polymorphic differences, yet they acknowledge that 2D6 is only a minor part of the metabolic pathway involved in oxycodone metabolism [11]. Predictably, the expressed genetic phenotype (extensive metabolizer, poor metabolizer, or ultrametabolizer) had little effect on dosing regimen, so the effect size was increased by adjusting the target serum concentration within the therapeutic range [11]. If the same therapeutic target is utilized (33 μg/L), the results are 5.6 mg for poor metabolizers (5 mg q.4h.), 8 mg for extensive metabolizers (7.5 mg q.4h.), and 9 mg for ultrametabolizers (10 mg q.4h.). For some practitioners, it may be difficult to comprehend the practical clinical relevance, yet this is mostly reflective of dose adjustment based upon a minor metabolic pathway [11]. The result would have been far different if Linares and colleagues had studied codeine or tramadol, both of which are prodrugs dependent on 2D6 metabolism for activation and eventual analgesic (or toxic) effect [13–15]. A predictable therapeutic range is not available for opioids. This is particularly true for chronic pain patients, for whom tolerance and cross-tolerance must be considered when dosing chronically and supplementing for breakthrough or incidental pain in both the ambulatory and acute care settings. No experienced pain practitioner would believe that oxycodone 5–10 mg dosed every 4 hours would 723
Fudin and Atkinson adequately treat every patient’s pain regardless of 2D6 phenotype. This is in part due to polymorphic differences in opioid-receptor makeup among various patients [16,17]. The authors’ listed oxycodone serum concentration (20–50 μg/L) originates from toxicology data collected by Schulz et al., who acknowledge these values are meant only as orientation because reported values are a single serum concentration without perspective of time between drug intake and blood sample, dosing interval, accumulation, tolerance, or involvement of concomitant diseases [11,18]. Therefore, therapeutic ranges listed for each drug are meant only as a guide, not as targets for dosing. Current practical obstacles for implementation of pharmacogenomic testing and commensurate therapeutic drug monitoring (TDM) include cost, laboratory turn-around time, clinician availability and calculation time (with or without electronic computing aids), and lack of expertise in interpretation among the vast majority of clinicians. Deviations in drug metabolism can have serious consequences or enormous value as part of a patient’s clinical profile. Likewise, TDM is significantly helpful in the evaluation of therapeutic response, drug interactions, and compliance. Extensive research is necessary, however, to apply pharmacogenomic testing and TDM to target a specific therapeutic range, because population-based studies documenting more than a single serum level do not exist. Moreover, as this is an area where incorrect calculations could result in patient harm or even death, it is necessary for studies to include chronic pain patients, not just healthy volunteers, and measure dose-response with frequent TDM. In short, we agree that pharmacogenomics and TDM combined with clinical pharmacokinetics could have widespread applicability to improve patient outcomes, but it may be premature to coin the term “pharmacokinomics” for personalized oxycodone dosing without further evidence across a wide polymorphic sample. Nevertheless, Linares and colleagues [11] offer a very exciting glimpse into a future where “pharmacokinomics” could or should be considered—one where biotechnology and medicine have become intertwined with improved access to electronic records and where necessary computational tools have become embedded ubiquitously as part of the charting process. The authors deserve much praise for their innovative foresight into the future of medicine, particularly its integration of medicine, clinical pharmacy, and pharmacokinetics. Such exciting collaboration across scientific, mathematical, pharmaceutical, and medical disciplines exemplifies the principle that alterum alterius auxilio eget, “each needs the help of the other,” for the advancement of medication safety and improved patient outcomes.
JEFFREY FUDIN, BS, PharmD, FCCP*†‡ TIMOTHY J. ATKINSON, PharmD§ *Adjunct Assistant Professor of Pharmacy Practice School of Pharmacy University of Connecticut 724
Storrs, Connecticut, USA Adjunct Associate Professor of Pharmacy Practice & Pain Management Albany College of Pharmacy & Health Sciences Albany, New York, USA ‡ Clinical Pharmacy Specialist and Director PGY2 Pain & Palliative Care Pharmacy Residency Stratton VA Medical Center Albany, New York, USA § Resident PGY2 Pain & Palliative Care Pharmacy Residency Stratton VA Medical Center Albany, New York, USA
†
References 1 Jonas D, Wines R. Pharmacogenomic testing and the prospect of individualized treatment. N C Med J 2013;74(6):485–93. 2 Polk B, Becker M. Pharmacogenomics in childhood rheumatic disorders: A foundation for future individualized therapy. Discov Med 2013;16(90):267–75. 3 Liu X, Cheng D, Kuang Q, et al. Association between UGT1A1*28 polymorphisms and clinical outcomes of irinotecan-based chemotherapies in colorectal cancer: A meta-analysis in Caucasians. PLoS One 2013;8(3):e58489. 4 Chen J, Zhao Q, Shi G, Wang L. XRCC1 Arg399Gln and clinical outcome of platinum-based treatment for advanced non-small cell lung cancer: A meta-analysis in 17 studies. J Zhejiang Univ Sci B 2012;13(11):875– 83. 5 Gamazon E, Lamba J, Pounds S, et al. Comprehensive genetic analysis of cytarabine sensitivity in a cellbased model identifies polymorphisms associated with outcome in AML patients. Blood 2013;121(21): 4366–76. 6 Molto J, Xinarianos G, Miranda C, et al. Simultaneous pharmacogenetics-based population pharmacokinetic analysis of darunavir and ritonavir in HIV-infected patients. Clin Pharmacokinet 2013;52(7):543–53. 7 Kagan R, Johnson E, Siaw W, et al. A genotypic test for HIV-1 tropism combining Sanger sequencing with ultradeep sequencing predicts virologic response in treatment-experienced patients. PLoS ONE 2012; 7(9):e46334. 8 Carlyn CJ, Baltch AL, Smith RP, et al. HIV genotyping for optimizing therapy. Presented at the Infectious Disease Society of America 38th Annual Meeting, New Orleans, Louisiana, September 7–10, 2000. 9 Wang W, Wang N, Wang J, Wang Z, Wu R. Delivering systems pharmacogenomics towards precision medicine through mathematics. Adv Drug Deliv Rev 2013;65(7):905–11.
Editorial 10 Arab-Alameddine M, Decosterd L, Buclin T, Telenti A, Csajka C. Antiretroviral drug toxicity in relation to pharmacokinetics, metabolic profile and pharmacogenetics. Expert Opin Drug Metab Toxicol 2011;7(5): 609–22. 11 Linares O, Daly D, Linares A, Setefanovski D, Boston R. Personalized oxycodone dosing: Using pharmacogenetic testing and clinical pharmacokinetics to reduce toxicity risk and increase effectiveness. Pain Med 2014. doi:10.1111/pme.12380. [Epub ahead of print]. 12 DiPiro J, Spruill W, Wade W, Blouin R, Pruemer J. Concepts in Clinical Pharmacokinetics, 4th edition. Besthesda, MD: American Society of Health-System Pharmacists; 2005. 13 Ciszkowski C, Madadi P, Phillips M, Lauwers A, Koren G. Codeine, ultrarapid metabolism genotype, and postoperative death. N Engl J Med 2009;361(8): 827–8.
14 Madadi P, Ross C, Hayden M, et al. Pharmacogenetics of neonatal opioid toxicity following maternal use of codeine during breastfeeding: A case–control study. Clin Pharmacol Ther 2009;85(1):31–5. 15 Gan S, Ismail R, Wan Adnan W, Zulmi W. Impact of CYP2D6 genetic polymorphism on tramadol pharmacokinetics and pharmacodynamics. Mol Diagn Ther 2007;11(3):171–81. 16 Pasternak G. Opioids and their receptors: Are we there yet? Neuropharmacology 2014;76:198– 203. 17 Pasternak G, Pan Y. Mu opioids and their receptors: Evolution of a concept. Pharmacol Rev 2013;65(4): 1257–317. 18 Schulz M, Schmoldt A. Therapeutic and toxic blood concentrations of more than 800 drugs and other xenobiotics. Pharmazie 2007;58:447–74.
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