Personalized medicine, also known as precision

Transcription

1 PRINTER-FRIENDLY VERSION AVAILABLE AT PAINMEDICINENEWS.COM Personalized Pain Medicine: Pharmacogenetic Testing for Pain and Opioid Addiction INNA BELFER, MD, PHD Adjunct Professor Departments of Medicine and Human Genetics University of Pittsburgh School of Medicine Pittsburgh, Pennsylvania Dr. Belfer reported no relevant financial disclosures. Personalized medicine, also known as precision medicine, is a young and rapidly growing field of health care. It aims to overcome many treatment concerns, such as unsatisfactory outcomes, variable response to standard treatment, and an inability to predict clinical outcomes in an individual patient. The current concept of personalized medicine looks to provide the right patient with the right drug at the right dose at the right time, by tailoring medical treatment to unique personal characteristics (eg, clinical, genetic, genomic, and environmental), values, and preferences during all stages of care (ie, prevention, diagnosis, treatment, and follow-up). Specific advantages of this approach include: higher likelihood of desired outcomes; reduced probability of adverse effects (AEs) from a drug or procedure; improvement of the clinical decision-making process for both patient and care provider; and a focus on preemptive and preventive strategies rather than reacting to a disease and associated disability. This review discusses how personalized medicine can be applied to the management of human pain and pain-related aberrant behaviors, specifically prescription drug abuse and opioid addiction. PAIN MEDICINE NEWS SEPTEMBER

2 Pain and Narcotic Addiction: Two Public Health Epidemics Chronic pain affects 100 million Americans every year more than diabetes, heart disease, stroke, and cancer combined. 1 These numbers are expected to escalate, in large part due to a doubling 2 of the older population, which has an increased incidence and prevalence of certain pain syndromes. 3 Research shows that up to 75% of hospitalized cancer patients experience moderate to severe pain 4 ; 20% of American adults report painrelated sleep disruptions 5 ; and the majority of chronic pain patients have compromised health status, anxiety, and depression. 6 Severe acute pain is present in over 30% of patients following surgery, affecting recovery and costs, and may lead to chronic postsurgical pain. 7 Unresolved pain places an overwhelming emotional, physical, and financial burden on patients and their families, and can be linked to $600 billion in health care costs, rehabilitation, and lost productivity. 8 Uncontrolled pain leads to avoidance behaviors, decreased mobility, and altered functional status. These, in turn, may cause diminished self-efficacy and social limitations that increase pain suffering, creating an endless cycle. 9 It is difficult to overestimate the impact of unresolved pain on daily life, including decreased functional capacity, diminished relationships, sexual dysfunction, frustration, and suicide risk. 10 Despite advances in pain science, the treatment of pain is always a clinical challenge because of its multifactorial etiology and pathophysiology, a variety of modulating factors, and potential comorbidities. The recent extraordinary increase in the prescribing of opioid analgesics has been an attempt to better control severe acute and chronic pain. Opioids are the most commonly prescribed drug category in the United States; 15% to 20% of physician office visits now include the prescription of an opioid, and 4 million Americans each year are prescribed a long-acting opioid. 11,12 However, the increased need for pain management has led to a rise in opioid misuse. 13 The National Survey on Drug Use and Health reported that the number of first-time abusers of prescription opioids increased from 628,000 in 1990 to 2.4 million in 2004; ER visits involving prescription opioid abuse increased by 45% from 2000 to 2002; and treatment admissions for the primary abuse of prescription opioids increased by 186% between 1997 and In recent years, the consumption of opioid medications has increased 300%, and deaths associated with opioid poisoning have more than tripled. 15 Prescription opioid pain relievers now account for more overdose deaths than heroin and cocaine combined. 16 The particular concern in pain medicine is remarkable interindividual variability in patient response to treatment, specifically to opioids, ranging from desirable pain relief to inadequate pain control or the development of opioid addiction and related aberrant drug-related behaviors. Although opioid therapy helps many patients improve function, use fewer health care services, and experience less pain-related distress, it can also cause dependence and addiction in biologically and psychosocially vulnerable individuals. 17 Addiction, defined as a chronic behavioral pattern of drug dependence and abuse associated with an underlying neurobiological dysfunction, 18,19 is a common outcome of opioid treatment. 20 Currently, a physician s ability to identify a patient who will develop an addiction to a pain medication is limited, since it based on subjective self-reported data of personal and family medical histories as well as environmental factors. Even accurately provided, these data represent only a portion of the array of important risk factors influencing the development of addiction. Studies show that genetic make-up may significantly contribute to the individual s susceptibility to both pain and addiction. Is it possible, then, to more accurately predict clinical outcomes from pain treatment with the right genetic tools? Genetic Approach to Pain, Analgesia, and Opioid Addiction The study of pain genetics is based on enormously unpredictable and unexplained interindividual variability in the expression of pain phenotypes across any painful disease or pain-causing event, such as postsurgical pain, sciatica, diabetic neuropathy, or musculoskeletal pain. Patients vary in pain perception (eg, severity, duration, location, and frequency of a typical episode), pain-coping behavior (eg, somatization and catastrophizing), and response to analgesics. 21 Some sources of this variability are well established. Demographic (eg, race, age, sex), environmental (eg, nutrition, stress), clinical/medical (eg, the degree of trauma), psychological (eg, mood, sleep patterns), and physiologic (eg, an endogenous inhibitory system) factors all affect pain processing and perception. 22 Known risk factors may explain only a small part of overall variability in the clinical manifestation of pain conditions. 23 Studies in healthy subjects reveal a wide range of self-reported pain scores in response to nociceptive stimulation, demonstrating that human pain sensitivity is variable. 24 Twin studies of experimental pain phenotypes, such as pressure pain and cold-pressor test thresholds, report heritability estimates up to 60%, pointing to predominantly genetic causes of variation in these pain modalities. 25 Similar estimates were shown in twin studies for clinical pain conditions: For all definitions of pain, there was a consistent excess concordance in monozygotic compared with dizygotic twins, equating up to 68% heritability for low back pain and up to 58% for neck pain. 26,27 Whereas there is evidence for substantial common genetic risk across many clinical pain conditions, different 2 PAINMEDICINENEWS.COM

3 pain phenotypes appear to be associated with different genetic factors, and many genes may affect pain independently, jointly, and/or through interaction with environmental factors. In general, one can conclude that up to half of inter-subject variation in a given pain phenotype is due to genetics, and susceptibility to a pain condition may be explained at least partly by genetic polymorphisms. Thus, identifying the unique and overlapping genetic profiles underlying clinical heterogeneity in pain syndromes and symptoms may lead to categories of patients based on genetic background and to the development of novel treatment strategies based on molecular mechanisms, including gene therapy, pharmacologic manipulation, and modulation of gene effects via their interaction with environmental factors. The first step in studying the genetics of pain was made using a linkage approach in families with rare pain disorders, such as hereditary sensory (and autonomic) neuropathies (types I-V); primary erythermalgia; paroxysmal extreme pain disorder; and congenital insensitivity to pain. For each of these conditions, a rare single mutation causing loss or gain of function of the gene has been identified. 28 Understanding the etiology of these Mendelian heritable disorders has provided insights into general human pain mechanisms and suggested new analgesic drug targets. One particular gene, SCN9A encoding Nav1.7 sodium channel, was of special interest because it was found to be responsible for 3 human pain disorders. Rare SCN9A mutations cause a complete absence of pain, whereas rare activating mutations cause severe episodic pain in paroxysmal extreme pain disorder and primary erythermalgia. 29 These findings suggest that common mutations in SCN9A may play a role in pain perception in the general population. A study in 5 independent cohorts with chronic painful diseases such as sciatica, phantom limb pain, post-diskectomy pain, and pancreatitis as well as in healthy subjects tested for experimental pain confirmed this hypothesis, showing that individuals experienced different amounts of pain, per nociceptive stimulus, on the basis of their SCN9A rs genotype. 30 Other studies using the same genetic approach analysis of the association between pain traits and common single nucleotide polymorphisms (SNPs) in candidate genes selected from animal or human linkage pain research revealed a polygenic nature of general pain similarly to other complex traits (such as psychiatric disorders), in which multiple genetic factors contribute to the variation in the phenotype. Each of these factors (eg, gene alleles) may have a small effect on a particular phenotype, but the combined effect from several genes may play a bigger role. In addition, a drug targeting a candidate gene may provide significant analgesia regardless of the proportion of variability in pain-related traits explained by polymorphisms in this gene. 31 Over the past decade and a half, the genetics of human pain have been extensively investigated using both candidate gene association studies and large-scale genome-wide association studies that are unbiased by gene prioritization. Despite the inherent challenges with reproducibility of the findings, data management, analytical techniques, and follow-up functional studies, there has been accelerating progress in the identification of genes involved in pain processing and perceiving pathways, 32 and a pain genome has been established (Table 1). 33 Some of these genes affect susceptibility to a disease that may be painful (eg, osteoarthritis, temporomandibular disorder), while others affect susceptibility to more severe or more chronic pain given a uniform pathology (eg, within the same clinical or experimental pain condition). Several genes were reported as reducing pain or as being protective from pain (Table 2). 30,34-40 Recent genetic studies of common pain conditions revealed more specific (or more complex) aspects of Table 1. Genes Involved in the Pain Genome 34 Table 2. Genes That May Reduce or Protect From Pain 30,34-40 Type Cytokines Enzymes Example IL6, IL10, TNF COMT, GCH1, CYP2D6 COMT GCH1 encoding catechol-o-methyltransferase encoding guanosine triphosphate cyclohydrolase 1 Ion channels KCNS1, CACNG2, CACNA2D3 MC1R encoding melanocortin-1 receptor Receptors OPRM1, ADRA2, DRD2 OPRM1 encoding mu-opioid receptor Transporters DAT1, 5HTT, ABCB1 TRPV1 encoding transient receptor potential cation channel PAIN MEDICINE NEWS SEPTEMBER

4 the influence of genes on pain phenotypes. Genes may have combined effects on pain and analgesia, so that the presence of each allele is necessary for the manifestation of the outcome. A study of surgical patients found that heterozygous patients with OPRM1 A118G and COMT G1947A alleles consumed significantly less morphine than homozygous patients; this analgesic effect was observed only when both SNPs were considered. 41 Some genes may have modality-specific effects. For example, the effect of COMT on pain depends dramatically on the modality of noxious stimuli, with the strongest contribution from COMT functional haplotypes to chemical/inflammatory and thermal pain modalities but not mechanical stimuli or temporal summation phenomenon. 42 The putative mechanism of this specificity may reflect a complex interplay of differential contributions of enhanced adrenergic versus dopaminergic systems resulting from low COMT activity. 43 This gene also demonstrated sex-specific effects contributing to pain behavior much stronger in women than in men. 42 This evidence is in line with data on significant interactions between sex and OPRM1 functional alleles, showing that female carriers of a minor allele of the A118G genotype had 2.3 times as much pain as male carriers of this allele 12 months after lumbar disk herniation. 44 Thus, genes may have sex-biased effects (eg, COMT affects both sexes but to a different extent) or even sex-antagonistic effects (eg, OPRM1 increases pain intensity in one sex but has a protective effect in the other). Similarly, genes may affect pain in a race- or ethnicity-specific manner. For example, the GCH1 functional haplotype observed in blacks (but not in whites) was associated with sickle cell anemia pain contrary to that described in European Americans, so that GCH1 showed protective effects against chronic pain in one ethnicity and contributed to an increased risk for chronic pain in another. 45 Interestingly, sex and ethnicity may interact, modifying the genetic influence on pain in both clinical and experimental contexts. Research in a genetically mixed population evaluating the role of several candidate genes in human pain sensitivity revealed that gender, ethnicity, and temperament contributed to individual variation in thermal and cold pain sensitivity through interactions with TRPV1 and OPRD1 SNP. 46 Such interplay among genetic, demographic, and environmental factors was also shown for vasopressin-related pain pathways. A genetic association study in humans revealed the influence of AVPR1A SNP on capsaicininduced pain levels, but only in male subjects reporting stress at the time of testing. 47 The analgesic efficacy of the vasopressin analog desmopressin uncovered a similar 3-way (gene x sex x acute stress) interaction with the drug, as desmopressin inhibition of capsaicin-related pain was observed only in nonstressed male subjects carrying 2 copies of the AVPR1A SNP minor allele. This new knowledge of the specificity and complexity of the genetic contribution to pain is of great importance for the development of new therapeutic strategies and personalized pain management; it is also imperative for genetic studies of other traits and behaviors closely related to pain, such as opioid addiction. Opioid abuse and addiction are similar to other drugseeking behaviors, many of which tend to run in families, 48 suggesting the relevance of genetic factors. Twin studies have shown that heritability estimates of addictions range from 0.39 (hallucinogens) to 0.72 (cocaine), and heritability estimates for addiction are usually higher than for substance use. 49 Similar to pain, the genetic complexity of addictive behavior is mainly due to polygenicity, heterogeneity, epistasis, and gene x environment interactions. Overall, at least 50% of susceptibility to drug addiction is genetically determined, and for opiates the genetic component may exceed 70%. 50 Genes involved in vulnerability to opioid addiction include both opioid-specific genes and those that act on common pathways for addiction neurobiology such as anxiety, impulsivity, and reward. Several genes, including MAOA encoding monoamine oxidase, SLC6A4 encoding serotonin transporter, and COMT have been implicated in the shared genetic liability between addictions and other psychiatric disorders. 51 Genetic association studies have been used in opioid addiction research to identify genetic factors contributing to the risk for addiction, based on discoveries in animal pharmacobehavioral and genetic models or based on drug-specific pharmacokinetics and pharmacodynamics. For heroin dependence, evidence was reported of its association with multiple genes, including variable number tandem repeat (VNTR) polymorphism at the serotonin transporter (5HTT/SERT) locus, 52 SNPs in DRD1 and DRD3 encoding dopamine receptors, 53,54 novel SNPs in CREB1 encoding the transcription factor camp response element binding protein, 55 multiloci haplotype in GRM3 encoding metabotropic glutamate receptor, 56 SNPs in 5-HT2A encoding serotonin 2A receptor, 57 GABAA encoding gamma-aminobutyric acid receptor subunits, 58 and BDNF encoding brain-derived neurotrophic factor. 59 For opioid and cocaine addiction, polymorphisms in the genes coding for dopamine receptors and transporter, opioid receptors, endogenous opioid peptides, cannabinoid receptors, and serotonin receptors and transporter all appear to be associated with the phenotypic expression of heritable vulnerability once opioids or cocaine are consumed. 60,61 Recent genetic epidemiologic studies confirm the wide-ranging role of dopamine pathways, since polymorphisms in related genes affect nearly every opioid addiction phenotype, including early onset, 62 risk for opioid dependence 63 and transition to addiction, and opioid-induced pleasure response PAINMEDICINENEWS.COM

5 Opioid system genes are of particular interest for genetic studies of both pain and opioid addiction, since encoded receptors are main binding sites for endogenous and exogenous opioids that play a major role in nociception, pain perception, analgesia, and opioid tolerance and dependence. 69 The gene encoding μ-opioid receptor (MOR), OPRM1, has the most studied nonsynonymous amino acid changing variant rs (118A>G, Asn40Asp), with a minor allele more potent in β-endorphin binding and receptor activity. 66 This SNP showed associations with interindividual differences in pain scores, 67 and opioid and other substance dependence in diverse populations. 68,69 OPRM1 has other polymorphisms with functional consequences; analysis of 12 intronic SNPs spanning the gene locus in whites revealed an association of SNPs in intron 1 with drug dependence (cocaine and opioid), 70 positive response to heroin after first use, 71 and heroin addiction. 72 Another functional SNP (rs563649) located within an alternatively spliced OPRM1 isoform (MOR- 1K) has been identified. The MOR-1K isoform codes for 6TM OPRM1 isoform, which displays excitatory rather than inhibitory cellular effects, and that are characteristic of the canonical 7TM isoform; this excitatory function of MOR may contribute to opioid tolerance and addiction. 73 Other opioid receptors α, encoded by OPRD1 and α, encoded by OPRK1 also play important roles in the development and persistence of opioid addiction. OPRD1 rs SNP was associated with opioid addiction in whites, 74 while the intronic SNP rs predicted patient response among blacks to 2 common treatments for opioid dependence. 75 OPRK1 SNP G36T in exon 2 had a higher prevalence in subjects with heroin dependence than in healthy controls. 76 In fact, there are epistatic effects between different opioid receptor genes, such as a significant interaction between OPRK1 variants and A118G of OPRM1 in increased susceptibility to opioid addiction, 77 confirming the contribution of MOR ligands as well as dynorphin peptides to the development of addiction. In addition to opioid, dopamine, and serotonin pathways genes, hypothalamic-pituitary-adrenal axis genes also influence vulnerability to opioid addiction. For instance, a 4-SNP haplotype in the MC2R gene encoding the melanocortin receptor type 2, involved in regulation of adrenal cortisol secretion, had a protective effect against development of heroin addiction in Hispanics. 78 In recent years, genome-wide association studies have led to the discovery of numerous genetic variants for complex diseases, including pain syndromes and substance dependence (Table 3) Despite this progress, questions remain regarding the missing heritability of opioid addiction, since most of the genetic risk factors are hidden, and their effects may be masked by Table 3. Genetic Variants Attributed to Pain Syndromes Genetic Variant A region on chromosome 5p15.2, located upstream of chaperonin-containing-tcp1-complex-5 gene and downstream of FAM173B Common variants within the ALDH1A2 gene and with rare variants at 1p31 Variants in TAOK3 encoding the serine/threonineprotein kinase Polymorphisms within a linkage disequilibrium block that spans 2q33.3-2q34 Metabotropic glutamate receptors mglur6 and mglur8, nuclear receptor NR4A2 and cryptochrome 1 Association Associated with a 30% higher risk for chronic widespread pain 133 Associated with severe osteoarthritis 130 Correlated with increased acute postoperative pain and increased morphine requirements 131 Strongly associated with the need for postoperative opioid analgesics, such as morphine and fentanyl, after painful surgery 132 Evidence for involvement in heroin addiction 86 Calcium and potassium pathways genes Multiple associations with opioid dependence 85 PAIN MEDICINE NEWS SEPTEMBER

6 environmental modulation or epigenetic mechanisms. Some of these are factors other than SNPs or VNTR gene variations, such as copy number variations that harbor genes contributing to opioid and other substance dependence risks. 86 Similar to pain genes, opioid addiction related genes also have ethnicity- and sex-specific effects. A polymorphism in the core promoter region of prodynorphin gene (PDYN) was associated with heroin dependence risk in males only, 87 while functional alleles in 5HTT and MAOA may independently contribute to heroin dependence in female patients. 88 Three or four copies of PDYN promoter polymorphism protected against cocaine dependence in a Hispanic population, 82 but also increased risk for cocaine and alcohol codependence in blacks. 89,90 A strong and significant effect of NCK2 gene, coding for a member of NCK family of adaptor proteins, on susceptibility to opiate addiction was reported in men of African descent, and this effect may have racial and gender specificities. 91 Finally, the effect of genes on the development of opioid addiction is modulated by stress response. 92 The findings described illustrate an influence of genetic variation on opioid addiction and provide support for considering genetic factors in treatment and relapse prevention. Data from the studies on genes affecting both pain and addiction can predict analgesic and addiction treatment outcomes with regard to pharmacogenetics and pharmacogenomics. Pharmacogenetics in Management of Pain and Addiction In general, pharmacogenetics investigates a gene environment interaction whereby variation in a gene interacts with an exposure to a drug (the environment ) to alter a measurable phenotype related to drug efficacy or toxicity. 93 Pharmacogenomics investigates the role of intraindividual variation in gene expression and function in relation to drug response and drug behavior. 94 Both represent a revolutionary era in the history of pharmacology by personalizing treatment strategies based on individual differences in genetic/genomic make-up. They have overlapping goals, including identifying patients at increased risk for drug intolerance and drug drug interactions; explaining an adverse event and/or therapeutic failure; and developing genetic profiles to accurately predict which subjects will respond to or experience AEs from taking a specific drug. Pharmacogenetic tools and approaches have been recently applied to pain and opioid addiction research, with studies showing that differences in the efficacy of analgesics and drugs used for treatment are genetically controlled. Furthermore, variation in therapeutic responses may result from interaction of multiple genes from different biologic pathways. 95 Polymorphic genes in pain and addiction patients determine their metabolic profile, drug toxicity, decreased efficacy with increased dosing, lack of prodrug conversion, and prolonged or adverse drug effects. 96 Two Gene Super Families CYP The CYP super family consists of 57 CYP genes classified into 18 families and 44 subfamilies. 99 Some of these genes are highly polymorphic and have substantial clinical impact on pain management outcomes. For example, CYP2D6, with over 70 alleles and 130 genetic variations, is responsible for the conversion of codeine to morphine. Based on the number of CYP2D6 functional alleles (which reflect enzymatic activity), individuals can be classified into 4 phenotypic groups: poor metabolizers (PM), intermediate metabolizers (IMs), extensive metabolizers (EMs), and ultrarapid metabolizers (UMs). The frequency of these phenotypes varies significantly across ethnicities. 100 Accordingly, UMs may require higher doses to achieve therapeutic drug levels and effects, whereas PMs might be at increased risk for drug-related toxicity. Indeed, PMs showed reduced analgesic effects after codeine, 101 tramadol, 102 and oxycodone 103 administration, and a CYP2D6 UM phenotype has been associated with quicker analgesic effects but higher μ-opioid related toxicity after tramadol 104 and oxycodone 105 administration. Importantly, CYP activity may also be modulated by inhibitors mimicking a genetic deficiency in the PM phenotype; and drug interactions modulating the activity of the enzymes involved in the other pathways of elimination have to be taken into account, because they can modify the therapeutic index of a drug or its pharmacologic properties. 106 Another CYP family gene, CYP2C19, has 36 alleles and influences the metabolism of citalopram, barbiturates, and diazepam. 107 Studies show that CYP2C19 6 PAINMEDICINENEWS.COM

7 Genetic variation can affect the pharmacokinetic and/or the pharmacodynamic properties of pain and addiction medications. Pharmacokinetic parameters include drug absorption, distribution, metabolism, and excretion. 97 Genes influencing these processes involve mostly those encoding drug-metabolizing enzymes and drug transporters. In the former, 2 super families, cytochrome P450 (CYP) and uridine diphosphate glucuronosyltransferase (UGT), determine metabolism of the majority of analgesics and drugs for opioid addiction treatment. Apparently, there are substantial interindividual differences in the ability to metabolize these drugs, driven by genetic polymorphisms that may account for 10- to 10,000-fold variation in drug activity. 98 Descriptions are included in the sidebar below. Genes encoding transporters, such as ATP-binding cassette (ABC), prostaglandin-endoperoxide synthase, and serotonin, dopamine, and excitatory amino acid transporters also contribute to the pharmacokinetics of pain and addiction drugs. For example, ABCB1/MDR1 is a highly polymorphic gene, with 38 SNPs regulating the expression and activity of P-glycoprotein. Studies showed that variant alleles were associated with reduced pain but also a higher frequency of oxycodone AEs; 117 with interindividual differences in pain relief achieved by morphine 118 ; and with prolonged fentanyl-induced suppression of respiration rates. 119 These polymorphisms were also implicated in heroin dependence research and found to control methadone effectiveness. 120 Three-locus genotype pattern determined methadone dosing in 2 independent populations of patients in methadone maintenance. 121 Some evidence suggests that ABCB1 variants interact with OPRM1 123 and COMT, 123 affecting opioid consumption and side effects. Genes encoding cyclooxygenase-1 and 2 (PTGS1 and PTGS2, respectively) regulate outcomes of pain treatment with nonsteroidal anti-inflammatory drugs (NSAIDs) such as rofecoxib and ibuprofen. 124 The serotonin transporter gene 5HTT has a well-established functional triallelic polymorphism (5-HTTLPR), with low-expressing alleles associated with higher pain sensitivity thresholds, less chronic orofacial pain and better analgesia from remifentanil. 125 Similarly, the dopamine transporter gene (DAT) polymorphism, DAT1, contributes to cold pain sensitivity 126 and buprenorphine treatment outcomes. 127 Finally, the promoter SNP in excitatory amino acid transporter gene (EAAT2) affects the frequency of analgesic usage 128 as well as the personality trait of reward dependence (especially in women), 129 and thus may confer vulnerability to risktaking behavior related to opioid abuse and addiction. Pharmacodynamic variation can be caused by differences in receptor activity, receptor binding affinity, and receptor density. 130 Two major receptor signaling pathways involved in the pharmacodynamics of analgesics and opioid addiction drugs are MOR and COMT, and related genes have been used as targets in pharmacogenetic studies. Dosing requirements for treatment PMs may experience an increased frequency of AEs with these drugs. 108 The gene encoding the CYP3A4 enzyme has functional alleles that influence postoperative fentanyl analgesia. 109 Finally, the CYP2C9 gene, with at least 35 allelic variants, has 2 most common alleles (*2 and *3) determining 50% and 15% of enzymatic activity, respectively, and that contribute to prolonged action of nonsteroidal anti-inflammatory drugs (NSAIDs) and the risk for acute gastrointestinal bleeding in patients receiving NSAIDs. 110,111 CYP genes metabolize methadone, a synthetic opioid used for standard treatment of opioid addiction 112 ; genetic diversity may explain a large interindividual variation in the pharmacokinetics and response to methadone. Functional alleles in CYP3A4, CYP2B6, CYP2C19, CYP3A5, and CYP2C8 influence methadone disposition in patients on methadone maintenance therapy. 113 With regard to CYP2D6 genotypes, UMs were found to be largely unsuccessful in methadone therapy, 114 but have been reported to do well on buprenorphine, which is not significantly metabolized by CYP2D6. UGT The UGT family is another super family of metabolizing enzymes, with three subfamilies (eg, UGT1A, 2A, and 2B) that catalyze the glucuronidation reaction in a wide range of structurally diverse endogenous and exogenous chemicals (eg, morphine, antidepressants, and NSAIDs). Functional alleles in the UGT2B7 gene have strong catalyzing abilities toward morphine, reducing morphine-6-glucuronide/morphine ratios. 115 A promoter SNP in the UGT1A1 gene regulates acetaminophen glucuronidation and affects an individual s risk for acetaminophen-induced liver failure. 116 The clinical implications of these and other variant alleles from the UGT family still remain to be determined. PAIN MEDICINE NEWS SEPTEMBER

8 with morphine, fentanyl, sufentanil, alfentanil, and oxycodone are determined by OPRM1 functional alleles. For example, women homozygous for the minor allele of A118G SNP experienced twice as much pain compared with men 131 and required more fentanyl during labor. 132 Another SNP located in the 5 -UTR of a novel exon 13 containing isoform MOR-1K contributes to individual differences in opiate response. 133 In turn, COMT variants also influence morphine consumption, and the major allele of COMT, Val158Met, was associated with less effective morphine analgesia in cancer pain patients. 134 Variants in COMT and OPRM1 have joint effects on morphine clinical efficacy. 41,135 This interaction may be partly explained by the regulatory effect of the COMT alleles on the MOP receptor system, including MOR expression and density in the human brain. 136 Although some pharmacogenetic findings have not been replicated or appear to be controversial, much of the data in recent analgesic clinical trials are promising. COMT functional haplotypes were projected to serve as genetic predictors of treatment outcome with nonselective β-adrenergic antagonist propranolol, and indeed, noncarriers of COMT high-activity haplotype demonstrated a beneficial effect with propranolol on pain perception in patients with chronic musculoskeletal pain. 137 Similarly, the analgesic efficacy of desmopressin for capsaicin-induced pain in men was dependent on SNP alleles in the AVPR1A gene encoding the vasopressin-1a receptor. 138 These examples illustrate the importance and relevance of stratification of patients based on pharmacogenetics for clinical trials and targeted analgesia. The next step will be optimization of pain management based on the unique genetic profile of an individual patient. The pharmacogenetics-based approach to pain and opioid addiction management represents a potential tool to improve the effectiveness and the side-effect profile of therapy; however, well-designed prospective studies are needed to demonstrate superiority to conventional dosing regimens. With recent advances in multiplex genotyping technologies, bioinformatics strategies, and statistical and analytical platforms, we now enter a new epoch of reliable and affordable genetic testing. This approach, in combination with rigorous pain phenotyping and clinical exam, will allow a clinician to identify patients at risk for more severe and more chronic pain, or development of opioid addiction, and predict treatment outcomes and optimize a treatment plan for each patient. Genetic Testing for Personalized Pain and Addiction Medicine Genetic testing is detection of variations in germline DNA sequences, as well as products or effects arising from changes in heritable sequences, nwhich can predict significant health effects. 139 Genetic testing for pain prediction and treatment selection aims to improve patient safety by avoiding the wrong drug, increasing drug efficacy, improving patient compliance, and reducing side effects of therapy. It also may improve health care by preventing AEs, thus reducing associated costs. It may equip a clinician with personalized clinical decision algorithms based on reliable, actionable, simple, and informative genetic data. Currently, there are several commercially available clinical genetic tests for analgesia. Genotyping of CYP2D6/2C19 is widely offered by clinical labs. Some labs offer a single gene/single polymorphism screen for particular pain medications such as UGT1A1 testing for buprenorphine, ibuprofen, and morphine, or OPRM1 for morphine analgesia and naltrexone for opioid dependence treatment. 140 More comprehensive pain management panels that include CYP2D6, CYP2C9, CYP2C19, CYP3A4, CYP3A5, CYP1A2, CYP2B6, UGT1A1, MTHFR, VKORC1, OPRM1, and COMT common variants are available through fewer laboratories. GeneSight Analgesic (Assurex Health) is a genetic panel test that analyzes OPRM1 and 5 CYP genes to predict a patient s response to opioids, NSAIDs, and muscle relaxants commonly used to treat chronic pain. Test results are provided with a color-coded report based on efficacy and tolerability, which displays which medications should be used as directed, used with caution, or used with increased caution and more frequent monitoring. 141 Pain Medication DNA Insight (Pathway Genomics) is a panel test that identifies genetic variants in OPRM1, methylene tetrahydrofolate reductase gene (MTHFR regulating norepinephrine, dopamine, and serotonin precursor methylfolate) and f4 CYP genes predicting response to various opioids. The result report includes the genotype/snp for each gene included in the panel, along with a description of the toxicity risk, dose required, medication efficacy, and plasma concentration based on a certain genotype. 142 Millennium PGT (Millennium Health) is a genetic panel test for pain medication selection. The panel includes analysis of 11 genes, including COMT, MTHFR, OPRM1, and 6 CYP genes, as well as UGT2B15 and vitamin K epoxide reductase complex subunit 1 gene (VKORC1). The results are provided with a proprietary Millennium Analysis of Patient Phenotype report that provides decision support for medications that may be affected by the patient s genotype. 143 Genetic testing for pain management is currently considered investigational for all indications. The genetic tests mentioned are laboratory-developed and validated under the general regulatory standards of the Clinical Laboratory Improvement Act and are not subject to FDA approval. There are several challenges in the interpretation of pharmacogenetic findings that 8 PAINMEDICINENEWS.COM

9 limit clinical implementation of pain genetic testing. For example, the combined effect of CYP2D6, CYP2C19, and CYP2C9 genotypes on pain phenotype is not well understood. 144 Very few drugs are metabolized by a single enzyme, and many genes may contribute to each of the enzymes or the related signaling pathway. Genome-wide association studies (GWAS) using large well-phenotyped cohorts of pain patients and phenotypes related to different analgesics may reveal many more target variants that should be considered for genetic testing. 145 Other factors affecting drug metabolism (eg, patient age, comorbidities, concomitant medication) 146,147 may significantly influence the genetic effects, and their contribution should be included when interpreting the genetic data. The quality of genetic data may be a concern because diagnostic errors can occur from rare sequence variations. 148 Finally, people with shared ancestry and physical traits are most commonly categorized by ethnic or racial groups; however, this designation also implies common genetic ancestral backgrounds, which might affect therapeutic responses. 149 Large consortium-based sequencing studies using next-generation whole-genome sequencing may provide a diverse genome map of different admixed populations and account for ancestral genetic structure, complex haplotypes, gene gene interactions, and rare variants to detect and replicate novel pharmacogenetic loci. 150 Genetic testing for prediction of opioid addiction and outcomes from treatment options is a big clinical need. Screening tools have been developed for the assessment of risk for opioid abuse and addiction. Testing for polygenic predisposition to opioid addiction has focused on dopaminergic and GABAergic systems. It was shown that homozygous mutations in genes encoding dopamine d2 receptor (DRD2A1), dopamine d1 receptor (DRD148A/G), dopamine β-hydroxylase (DBH-1021 C/T), and GABA α-6 (GABAA6-1519T/C) may stratify opioid risk.151 Pyrosequencing-based screening utilized 11 loci in DRD2 spanning the whole range of the DRD2 gene locus up to the functionally related ankyrin repeat and kinase domain containing 1 gene (ANKK1), and may be useful for the genetic-based diagnosis of abuse and addiction behavior.152 Proove Opioid Risk Panel (Proove Biosciences) is a comprehensive opioid addiction risk assessment tool representing a panel of functional alleles from 12 genes involved in the mesolimbic reward system such as dopaminergic genes (eg, DRD1, DRD2, DRD4, DAT1, DBH, and COMT), serotoninergic genes (eg, 5HTT, 5HT2A, and MTHFR), GABAergic genes (eg, GABA), and opioid genes (eg, OPRM1 and OPRK1). This panel is intended to predict opioid abuse and failure of opioid therapy. Genetic testing results are provided along with an overall Dependence Risk Index. 153 Numerous studies confirmed the role of opioid system genes as moderators of the effects of addiction medications. Minor allele of OPRM1 A118G determined longer time to relapse in naltrexone-treated subjects,154 and OPRD1 rs predicted response to methadone or buprenorphine/naloxone.155 Presently, several clinical trials are investigating the utility of pharmacogenetic tools for analgesia, reducing analgesics AEs, and predictiing opioid treatment outcomes. One study assessed whether pharmacogenomic testing is associated with changes in how physicians manage patient medication regimens and changes in rates of adverse drug reactions, hospitalizations, and emergency department visits.156 Another study intends to assess whether gene polymorphisms are associated with variability in response to morphine among pediatric patients using a GWAS approach.157 The ultimate goal is to inform clinical decisions using the best therapeutic options for analgesia and addiction management. Genetic testing is expected to detect a particular genetic variant (or set of variants) for a particular phenotype (eg, pathologic pain or opioid addiction), in a particular population (eg, patients undergoing pain or addiction treatment), and for a particular purpose (eg, risk assessment or prediction of outcome). The value of genetic testing can be assessed by analytical validity (eg, ability of the test to accurately and reliably identify patients carrying the genotype of interest and exclude noncarriers), clinical validity (eg, ability of the test to detect or predict the presence or absence of a target phenotype), and clinical utility (eg, effect of test results on clinical outcome improvement). 139 Information on the analytical validity of pharmacogenetic testing for pain or addiction management is limited and may vary between gene assay panels. Information concerning the specifics of genetic methods and techniques used for data analysis is not provided through the manufacturers websites, and thus cannot be readily assessed. In contrast, the evidence on the clinical validity of pain or addiction pharmacogenetic testing is characterized by a large number of studies that evaluate associations of many different genetic variants and responses to analgesic medication, risk for adverse events, and addiction risk. The largest body of evidence is related to the association of the OPRM1 A118G SNP with analgesic response and opioid dependence, although with some discrepancy.158 For other genes included in commercially available pain or addiction management panels, evidence evaluating associations between polymorphisms and analgesic responses, AEs, or addiction risk is less established. For a comprehensive assessment of a given gene or panel of genes to demonstrate clinical utility, evidence is needed that testing for particular genetic variants leads to changes in clinical management that substantially improve outcomes, such as improved pain control, shorter time to PAIN MEDICINE NEWS SEPTEMBER

10 pain control, reduced frequency of AEs, or reduced rates of addiction. To date, the clinical utility of pharmacogenetic testing in pain and addiction management is not completely defined due to limited published data on the employment of pharmacogenetic testing in clinical pain practice. Pharmacogenetic testing for pain and addiction treatment has potential clinical utility in several settings, including drug selection or avoidance, and dose optimization. For drug selection, pharmacogenetic testing could be used to identify individuals not likely to respond to a particular drug, or individuals at high risk for an adverse drug reaction. For dose optimization, pharmacogenetic testing could identify individuals who are likely to be sensitive or resistant to a particular drug, or to estimate dose and dosing frequency. DNA-based testing is a relatively new and rapidly emerging field, and its integration into pain and addiction clinical management is a priority for both clinicians and patients. Public opinion on genetic testing has generally been favorable. A recent study looking at population experiences, beliefs, and expectations concerning genetic testing over the past decade confirmed that the awareness and reported use of genetic tests remained stable: More respondents expected genetic testing to become more widely applied, believed that knowledge about the genetic background of disease helps people live longer, and that testing should be promoted more intensively. Furthermore, people are more interested now in their own genetic make-up. 159 Overall, these results suggest that expectations of benefits and potential use of genetic testing have been raised among the public, and positive attitudes from a pain patient population may be expected. However, clear recommendations, guidelines and standards for quality assurance and clinical interpretation of newly developed genetic tests are needed to further advance their implementation in daily clinical practice in the pain and addiction fields. In summary, the crucial goal of pain medicine is adequate pain relief with minimal AEs. Pain, analgesia and addiction represent complex traits that are significantly controlled by genetic polymorphisms. A pharmacogenetics-based approach may identify patients at risk for poor clinical outcomes from pain and addiction treatment. Genetic testing is a powerful tool that can provide point-of-care genetic data for use in personalized predictive algorithms for clinical decision making in pain and opioid addiction management in individual patients. These tests are becoming more complex in terms of the technologies used and in their interpretation, and require new expertise for successful implementation in clinical practice. In addition, there are some unresolved ethical, legal, and social implications of genetic testing for pain. Despite this, there is a growing interest in the pain and addiction communities with regard to the rapid optimization of pain genetic testing services because they have the potential to dramatically improve the utility and efficacy of both current and future pain and addiction management strategies, 160 and serve as an essential basis for personalized pain medicine. References 1. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. Washington, DC: The National Academies Press, Institute of Medicine; Ortman JM, et al. Curr Popul Rep. May Kaye AD, et al. Ochsner J. 2010;10(3): Connors AF, et al. JAMA. 1995;274(20): National Sleep Foundation. Sleep in America poll Schaefer C, et al. Pain Med. 2014;15(12): McGrath B, et al. Can J Anaesth. 2004;51(9): Stewart WF, et al. JAMA. 2003;290: Webster LR. Pain Med. 2014;15(3): Okie S. N Engl J Med. 2010;363(21): Grady D, et al. Arch Intern Med. 2011;171(16): Rosenblum AI, et al. Exp Clin Psychopharmacol. 2008;16(5): SAMHSA. ED trends from DAWN Final estimates Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies; Substance Abuse and Mental Health Services Administration. Results from the 2010 National Survey on Drug Use and Health: volume 1: summary of national findings. Rockville, MD: Substance Abuse and Mental Health Services Administration, Office of Applied Studies; Centers for Disease Control and Prevention. Opioids drive continued increase in drug overdose deaths; 2013 [2014]. gov/media/releases/2013/p0220_drug_overdose_deaths.html. 17. Ballantyne JC, et al. Pain. 2007;129(3): NIDA. The Neurobiology of Drug Addition; gov/publications/teaching-packets/neurobiology-drug-addiction/ section-iii-action-heroin-morphine/10-addiction-vs-dependence. 19. NIDA. What is prescription drug abuse? gov/publications/research-reports/prescription-drugs/what-prescription-drug-abuse. 20. Dart RC, et al. N Engl J Med. 2015;372(3): PAINMEDICINENEWS.COM

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