¹ Proove Biosciences, Inc., Irvine, CA, USA;

² Spokane Spine Center, Spokane WA, USA

Corresponding Author: Lisa M. Davila, Proove Biosciences, Inc., 10820 Guilford Rd., Suite 201, Annapolis Junction, MD 20701, USA. Tel: 443-690-6560; Fax: 888-971-4219; E-mail: ldavila@proove.com.

Conflicts of Interest and Source of Funding: All authors with the exception of Katrina Lewis are employed by Proove Biosciences, Inc. Financial support for the research and writing of this paper was provided by Proove Biosciences, Inc.

	ABSTRACT
	INTRODUCTION
	METHODS
	GENETIC TESTING CHALLENGES
	CONCLUSION
	REFERENCES

	ABSTRACT

Published guidelines for treating injured workers include the need for personalized treatment to manage chronic pain symptoms and increase functional status. However, they often fail to clarify how to objectively personalize these treatments. Further, certain patients need analgesic relief beyond the short term. In these cases, it is not sufficient or reasonable to utilize the typical broad protocol-based justifications for reduction of opioids and other medications in a haphazard manner based purely on poor response, without attempting to elucidate possible pharmacogenetic reasons for this. These guidelines acknowledge the problem of substance abuse and set forth methods for treatment and prevention. Although it has been established in the scientific community that an individual’s experience of pain and likelihood for addiction both have genetic components, genetic testing is not routinely included as part of the overall treatment plan for injured workers with chronic pain. Because decisions in cases of workplace injury should be based on scientific evidence, genetic testing results can add some objective information to the existing subjective and objective clinical data; help ascertain the efficacy and potential for toxicity of treatment; and therefore provide more information for accurate clinical decisions. We propose the addition of genetic testing to consensus guidelines for treating injured workers in order to improve patients’ functional status, increase productivity, improve safety of prescribing, decrease the likelihood of substance abuse, and save on overall healthcare costs.

	INTRODUCTION

   Guidelines currently used in decision making for workplace injuries include the Guidelines for the Chronic Use of Opioids by the American College of Occupational and Environmental Medicine (ACOEM), the Official Disability Guidelines (ODG), and the Medical Treatment Utilization Schedule (MTUS) for the State of California.

   An expert multidisciplinary panel used evidence-based data to develop the ACOEM guidelines to manage injured workers whose pain has not been controlled by more conservative means. The ODG is a product of the Work Loss Data Institute (WLDI), an independent database development company focused on workplace health and productivity, and provides evidence-based disability duration guidelines and benchmarking data for reportable conditions. The MTUS is an adaptation of the ODG for the State of California. All of the guidelines contain detailed information about treating injured workers, including claimants who have long-term disability related to chronic non-cancer pain (CNCP) and opioid use.

   In this paper, we propose that such guidelines could be enhanced by integrating genetic testing into treatment plans for these claimants. We will describe challenges facing CNCP claimants and clinicians and describe how genetic information may guide treatment decisions and thus result in better outcomes for injured workers with CNCP.

   Workplace Injuries: Scope and Associated Sequelae

   National surveillance information indicates that there are about 3 million workers injured each year (1). The U.S. Bureau of Labor Statistics (BLS) reports that overall incidence of nonfatal occupational injury and illness cases requiring days away from work was 109.4 cases per 10,000 full-time workers in 2013 (2). Research shows, however, that many workplace-related injuries are not reported by employees or employers, and some are not captured by BLS or state-level workers compensation reporting bodies (3). Thus the resultant disability and time away from work may have a more profound effect on individual health; lost productivity; and indirect and direct costs.

   Chronic Pain from Workplace Injuries

   About one-third of work-related injuries necessitating days away from work are related to musculoskeletal conditions (2). Pain related to these injuries can be self-limited (acute-resolving when the injury resolves), subacute pain (usually defined as lasting 4 to 12 weeks) or become chronic pain, which persists for over 4 months or years. Among workers compensation (WC) claimants who have back pain, one of the most common CNCP conditions, up to 20% are still collecting benefits after at least one month following the date of injury (4).

   Opioids for pain: benefit or detriment?

   While opioids may have a pain-relieving benefit in the short-term for some musculoskeletal injuries, this class of medication is not specified in guidelines as a necessary treatment in the acute post-injury period (5). Further, opioids have not been shown to have a significant effect on CNCP (6, 7); nor have these drugs been associated with overall improved physical or emotional functioning. A recent systematic review showed that opioid therapy for CNCP patients has been associated with specific complications including fractures, myocardial infarction, sexual dysfunction, opioid abuse, and opioid overdose (8). WC claimants who take opioids have been shown to have prolonged disability and higher overall costs than claimants who were never prescribed these drugs (9, 10). This is the case especially in the case of long-acting opioids, such as OxyContin^® (11-14).

   Genetics, pain, and addiction

   Research evidence indicates that along with external factors, genetics have a significant effect on an individual’s pain perception (15, 16). In addition, the National Institute on Drug Abuse (17) and the American Society of Addiction Medicine (18) report that along with addiction-related environmental factors such as stress, sexual abuse, and drug availability, the likelihood for addiction is about 50% related to an individual’s genetic profile. Genetic variations including single nucleotide polymorphisms (SNPs) that affect neurochemistry in the mesocorticolimbic or “reward” pathway, have been implicated for their link to opioid reward, craving, and addiction (19-23).

   Despite growing knowledge in this area, genetic testing is not currently incorporated into the standard of care for injured workers described in the three sets of guidelines we reviewed. We believe that this neglect of genetic factors has in part contributed to opioid addiction; prolonged disability; delayed return-to-work rates among workers compensation claimants; and increased employer and payer costs.

   Personalized Medicine

   Over the past decade, there has been a concerted attempt to recognize the convergence of genetic factors and disease processes and to coordinate care accordingly. This concept is commonly referred to as personalized medicine (24), an approach that may be useful for CNCP management—especially in instances of opioid use. Within the field of personalized medicine, pharmacogenomics or pharmacogenetics refers to the practice of using results of genetic testing to guide medication management decisions (21, 25-31).

   Genetic testing to establish level of pain

   The use of genetic testing is mentioned in the consensus guidelines only with regard to diagnosing pain. The guidelines are clear that cytokine DNA testing should not be used to diagnose pain. MTUS guidelines, for example, are very explicit: “There is no current evidence to support the use of cytokine DNA testing for the diagnosis of pain, including chronic pain (32)”. There are, however, non-cytokine-based genetic tests based on the analysis of SNPs, such as catecholamine-O-methyltransferase (COMT), which can predict the likelihood of individual pain sensitivity (15, 16, 33-35).

   Genetic testing to screen for likelihood of opioid abuse

   Approximately 30% of WC claimants are taking opioids (36), putting a significant number of America’s workers at risk for addiction. Consensus guidelines explicitly recommend screening for addiction risk before to initiating opioid therapy (32). It is important to identify individuals who have the potential to develop aberrant drug use both prior to prescribing opioids and while actively undergoing this treatment. Such screening tends to occur after a claimant is already taking opioids on a chronic basis, and consists of screens for aberrant behavior/misuse (32) rather than intrinsic addiction risk.

   In the consensus guidelines, experts have established a validation threshold for substance abuse risk screening. Five screening methods are listed as options: The CAGE Questionnaire (37); Skinner Trauma Screen (38); Cyr-Wartman Screen (39); Screener and Opioid Assessment for Patients in Pain, Revised (SOAPP-R) (40); and the Opioid Risk Tool (ORT) (41). There are approximately 14 additional tools available. Studies show, however, that even when physicians use such screening tools, their ability to predict patients who misuse prescription opioid pain medications is no better than 50% (42, 43).

   Genetic Testing to Guide Long-term Opioid Use

   For long-term or chronic use of opioids-defined as longer than 6 months-several consensus guidelines provide instructive insights. The ACOEM guidelines indicate that screening for addiction risk should occur, but if opioids are providing pain relief and improved functioning, they should be continued, even if a patient is at risk for addiction (44). In this case, if genetic testing confirms an increased addiction risk, the patient may require closer monitoring.

   On the topic of acute or sub-acute pain ODG suggests that longer duration of use may lead to mental dependency; higher prevalence of work disability; depression; anxiety; and substance abuse (45). In such instances, genetics may need to be evaluated because results may objectively show a predisposition to addiction, lack of responsiveness to certain opioids, or potential for early tolerance. Again, the treatment plan may likely change.

   For chronic pain, the ODG suggests opioids only as a second- or third-line option at doses less than or equal to 120 milligrams daily oral morphine equivalent dosage in patients not at risk of misuse or diversion. Genetic studies suggest, however, that patients with certain gene variants may require less morphine equivalent doses to achieve the same analgesic response as wild type patients, obviously dependent on which opioid is utilized (46). In addition, drug tolerance is associated in part with how medications interact and are metabolized (47) and genetic testing may provide information about the activity of enzymes involved in drug metabolism (48). This includes the role of other non-analgesic medication metabolites and competing pathways in individual patient polypharmacy to delineate possible objective reasons for poor patient response to specific medication regimes and drugs. This might provide further impetus for rationalization by providers of medication regimens, and thereby improve on both safety and costs.

	METHODS

   Framework for Adding Genetic Testing to Inform Decisions Prior to Opioid Therapy Consideration

   We propose a framework that integrates genetic testing in the following way (Table 1):

   1. Attempt to determine if the pain is nociceptive or neuropathic and if there are underlying contributing psychological issues (32). Despite imaging studies and other diagnostics, CNCP can appear to have no discernible cause (5) thus differentiating between the two types of pain may be problematic. Nociceptive pain is a response to injury and can arise from somatic or visceral pain receptors. Opioids tend to be effective in relieving this type of pain. Neuropathic pain, on the other hand, signals damage to nerves but nevertheless opioids tend to be ineffective. Consequently, if patients with neuropathic pain take opioids for an extended period of time, they can respond poorly or develop hyperalgesia (49).

   Patients with neuropathic pain, however, have been shown to experience improved pain relief with selective serotonin reuptake inhibitors (SSRIs) and anticonvulsants in some instances (50). This may be due in part to pain pathways that differ from those of nociceptive pain. Research using rat models shows that neuropathic pain results in changes in the serotonin and dopamine systems, which may also be an early sign of chronic maladaptation to ongoing pain (51).

   Genetic tests can reveal information regarding specific SNPs in these serotonin and dopamine pain pathways. Hence clinicians have objective data to aid in determining whether a patient is prone to a particular etiology of pain, and their metabolism and response to certain anticonvulsants and SSRIs, which may help to avoid trial-and-error treatments (52, 53).

Along with determining the type of pain, a comprehensive psychological assessment may reveal the presence of disorders that are commonly associated with CNCP including depression, anxiety, and bipolar disease (54-58). Treating co-morbidities can improve pain perception, lower pain responses, and thereby improve psychological functioning (59), but in some instances the clinician may not be aware that a patient has a co-occurring psychiatric disorder. Genetic testing can show if a patient has likelihood for one or more comorbidities. Numerous studies have found significant associations between specific SNPs and psychiatric conditions including substance abuse (Table 2).

   2. Attempt non-opioid analgesic trial prior to opioids (32). Genetic testing may influence the choice of non-opioid analgesics. For example, testing for SNPs that affect cytochrome P450 enzymes may influence response or toxicity to nonsteroidal anti-inflammatory medications (NSAIDS), which are commonly prescribed as a first-line treatment for pain (60). To screen for risk of NSAID-related gastrointestinal bleeding, physicians could determine the presence of a cytochrome P450, family 2, subfamily C, polypeptide 9 genotype that has been associated with this complication (61). Again, more careful selection of patients at risk for GI bleeding with NSAIDs could help avoid this potentially catastrophic and expensive complication.

   3. Set goals for opioid therapy and establish a written pain agreement to improve documentation of patient education, the treatment plan, and informed consent (32). Prescription drug monitoring programs and other initiatives that help detect drug abuse can be useful for detecting medication-related problems after they have occurred, but prevention, of course, is ideal (62). Patient education can be enhanced by discussions about how an individual’s personal genetic profile may provide insights into the risk of opioid abuse and how genes affect drug metabolism. This, along with the addition of genetic testing to informed consent forms, could make such pain agreements comprehensive and more meaningful to patients.

   4. Perform baseline pain and functional assessments (32). Assessment of function should include social; physical; and psychological factors; and also daily and work activities, and should be performed using a validated instrument or numerical rating scale (32). As clinical recovery may be dependent upon identifying and addressing previously unknown or undocumented medical and/or psychosocial issues (63), genetic testing for relevant SNPs may provide insights into undiagnosed conditions that directly affect physical or emotional functioning, including heightened pain sensitivity and the quality of a patient’s coping mechanisms (64). This could help guide decisions in regards to referral of certain patients for more intensive cognitive behavioral therapy, which has a strong evidence base for efficacy in CNCP (65). A patient’s risk of opioid abuse should be also be included as part of the genetic assessment, as numerous studies support the link between mesolimbic SNPs and opioid abuse (66).

   5. Assess the likelihood that the patient could be weaned from opioids if there is no improvement in pain and function (32). Because genetics may correlate with opioid dependence, they are likely to contribute to a patient’s ability to be weaned from such medication (67). Weaning or discontinuing opioid therapy without a functional restoration or alternative maintenance regimen (either buprenorphine or methadone) for CNCP patients has been characterized by high dropout rates (68) and can be hindered by the presence of diagnosed or undiagnosed psychiatric conditions (69), inadequate emotional support (70) or the inability to cope with withdrawal symptoms (71). There is also a lack of formal evidence-based opioid-weaning clinical guidelines for clinicians’ use (72). Thus, having information about SNPs that may affect withdrawal severity, or indicate the likelihood of maintenance therapy success may help clinicians develop a plan that will help patients maintain physical and emotional comfort and develop effective coping mechanisms while tapering off opioids.

   6. Consider the use of a urine drug screen to assess for the use or the presence of illegal drugs. Genetic variations in drug metabolizing enzymes can influence the metabolism of medications, thus affecting the accuracy of urine drug screening results (73). Therefore, it may be appropriate to consider this form of genetic testing to explain idiosyncratic results (74).

   7. Use a consistent pharmacy (75).

   8. The physician should discuss the risks and benefits of controlled substances and other treatment modalities with the patient, caregiver, or guardian (32). Discussing the risks and benefits of opioid therapy with the patient may lessen the chances of abuse or diversion of the prescribed opioid (62). In addition, there have been legal precedents established with regard to genetic or hereditary conditions that support inclusion of genetic testing in such discussions and/or written agreements (76, 77). Courts have determined that physicians must disclose all risks to patients, and ignorance does not excuse responsibility.

	GENETIC TESTING CHALLENGES

   Genetic testing to guide chronic pain treatment is not yet routine and more prospective outcome studies on the value of this practice are needed. Further, some experts believe that genetic information is only moderately predictive of the likelihood of addiction and that such information is being used prematurely before it has been shown to be a valid part of a treatment plan. However, emerging data shows that genetic prediction of addiction risk maybe far superior to the conventional validated risk tools mentioned above and currently in use. Nevertheless, we believe pharmacogenomics has potential value for individualizing patient treatment. Genetic testing, as part of the course of treatment, can give clinicians objective data for assessment of disease risk, diagnosis, and medication response—all of which could lead to better pain relief and improved functioning for CNCP patients.

	CONCLUSION

   With the emerging consensus that genetic predisposition is involved in pain perception and about half of substance abuse cases, it appears plausible that genetic testing could play an important role in guiding treatment decisions in chronic pain therapies for injured workers, especially if opioid therapy is already underway or being considered. Evaluating a patient’s genetic profile may give clinicians objective information on which to base treatments, thereby decreasing pain; improving functioning; improving safety by reducing drug interactions, complications, and toxicities; helping return claimants to work; and saving costs for employers and payers.

	REFERENCES

1. Probst TM. Organizational Safety Climate and Supervisor Safety Enforcement: Multilevel Explorations of the Causes of Accident Underreporting. J. Appl. Psychol. 2015; No Pagination Specified.
2. BLS. Nonfatal occupational injuries and illnesses requiring days away from work, 2013. Washington DC: U.S. Bureau of Labor Statistics. 2014; USDL-14-2246.
3. Boden LI, Ozonoff A. Capture–Recapture Estimates of Nonfatal Workplace Injuries and Illnesses. Ann. Epidemiol. 2008; 18 (6): 500-506.
4. Steenstra I, Busse J, Tolusso D, et al. Predicting Time on Prolonged Benefits for Injured Workers with Acute Back Pain. J. Occup. Rehabil. 2015; 25 (2): 267-278.
5. Chou R, Qaseem A, Snow V, et al. Diagnosis and treatment of low back pain: a joint clinical practice guideline from the American College of Physicians and the American Pain Society. Ann. Intern. Med. 2007; 147 (7): 478-491.
6. Knaggs RD. SP0074 The Global Burden of Use and Abuse of Opiods in Non-Malignant Pain. Ann. Rheum. Dis. 2015; 74 (Suppl 2): 20.
7. Robinson JP, Dansie EJ, Wilson HD, Rapp S, et al. Attitudes and Beliefs of Working and Work-Disabled People with Chronic Pain Prescribed Long-Term Opioids. Pain Med. 2015; 16 (7): 1311-1324.
8. Chou R, Turner JA, Devine EB, et al. The effectiveness and risks of long-term opioid therapy for chronic pain: a systematic review for a National Institutes of Health Pathways to Prevention Workshop. Ann. Intern. Med. 2015; 162 (4): 276-286.
9. Tao X, Lavin RA, Yuspeh L, Weaver VM, et al. The Association of the Use of Opioid and Psychotropic Medications With Workers’ Compensation Claim Costs and Lost Work Time. J. Occup. Environ. Med. 2015; 57 (2): 196-201.
10. Faour M, Anderson JT, Haas AR, Woods ST, et al. Preoperative Duration of Opioids Use and Return-to-Work (RTW) Rates after Multilevel Cervical Fusion in Workers’ Compensation (WC) Setting. The Spine Journal. 15 (10): S120-S121.
11. Bernacki EJ, Yuspeh L, Lavin R, Tao XG. Increases in the use and cost of opioids to treat acute and chronic pain in injured workers, 1999 to 2009. J. Occup. Environ. Med. 2012; 54 (2): 216-223.
12. Lavin RA, Tao X, Yuspeh L, Bernacki EJ. Temporal relationship between lumbar spine surgeries, return to work, and workers’ compensation costs in a cohort of injured workers. J. Occup. Environ. Med. 2013; 55 (5): 539-543.
13. Tao XG, Lavin RA, Yuspeh L, Bernacki EJ. Impact of the combined use of opioids and surgical procedures on workers’ compensation cost among a cohort of injured workers in the state of Louisiana. J. Occup. Environ. Med. 2012; 54 (12): 1513-1519.
14. Tao XG, Lavin RA, Yuspeh L, Bernacki EJ. Natural history of opioid dosage escalation post-injury: a cohort study of injured workers in the State of Louisiana. J. Occup. Environ. Med. 2012; 54 (4): 439-444.
15. Diatchenko L, Fillingim RB, Smith SB, Maixner W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat. Rev. Rheumatol. 2013; 9 (6): 340-350.
16. Diatchenko L, Nackley AG, Slade GD, Fillingim RB, Maixner W. Idiopathic pain disorders--pathways of vulnerability. Pain. 2006; 123 (3): 226-230.
17. NIDA. Addiction Science: From Molecules to Managed Care. 2008; www.drugabuse.com/publications/addiction-science/introduction.
18. ASAM. Definition of Addiction. 2015; http://www.asam.org/for-the-public/definition-of-addiction Accessed October 3.
19. Bauer IE, Soares JC, Nielsen DA. The role of opioidergic genes in the treatment outcome of drug addiction pharmacotherapy: A systematic review. The American Journal on Addictions. 2015; 24 (1): 15-23.
20. Patriquin MA, Bauer IE, Soares JC, Graham DP, et al. Addiction pharmacogenetics: a systematic review of the genetic variation of the dopaminergic system. Psychiatr Genet. 2015; 25 (5): 181-193.
21. Somogyi AA, Coller JK, Barratt DT. Pharmacogenetics of Opioid Response. Clin. Pharmacol. Ther. 2015; 97 (2): 125-127.
22. Bauer IE, Graham DP, Soares JC, Nielsen DA. Serotonergic gene variation in substance use pharmacotherapy: a systematic review. Pharmacogenomics. 2015; 16 (11): 1305-1312.
23. Volkow ND, Wang GJ, Fowler JS, Tomasi D. Addiction Circuitry in the Human Brain. FOCUS. 2015; 13 (3): 341-350.
24. Hamburg MA, Collins FS. The path to personalized medicine. N. Engl. J. Med. 2010; 363 (4): 301-304.
25. Kapur BM, Lala PK, Shaw JL. Pharmacogenetics of chronic pain management. Clin. Biochem. 2014; 47 (13-14): 1169-1187.
26. Lindpaintner K. Pharmacogenetics and the future of medical practice. Br. J. Clin. Pharmacol. 2002; 54 (2): 221-230.
27. Ma Q, Lu AY. Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol. Rev. 2011; 63 (2): 437-459.
28. Mroziewicz M, Tyndale RF. Pharmacogenetics: A Tool for Identifying Genetic Factors in Drug Dependence and Response to Treatment. Addict. Sci. Clin. Pract. 2010; 5 (2): 17-29.
29. Rollason V, Samer C, Piguet V, Dayer P, et al. Pharmacogenetics of analgesics: toward the individualization of prescription. Pharmacogenomics. 2008; 9 (7): 905-933.
30. Smith MT, Muralidharan A. Pharmacogenetics of pain and analgesia. Clin. Genet. 2012; 82 (4): 321-330.
31. Svetlik S, Hronova K, Bakhouche H, Matouskova O, et al. Pharmacogenetics of chronic pain and its treatment. Mediators Inflamm. 2013; 2013: 864319.
32. MTUS. Medical Treatment Utilization Schedule: Chronic Pain Medical Treatment Guidelines. 2009.
33. Nackley AG, Tan KS, Fecho K, Flood P, et al. Catechol-O-methyltransferase inhibition increases pain sensitivity through activation of both beta2- and beta3-adrenergic receptors. Pain. 2007; 128 (3): 199-208.
34. Diatchenko L, Slade GD, Nackley AG, et al. Genetic basis for individual variations in pain perception and the development of a chronic pain condition. Hum. Mol. Genet. 2005; 14 (1): 135-143.
35. Onojighofia T. Perception of analgesia in narcotic users with chronic pain: A multi-center cross-sectional study comparing genotype to Pain VAS (P.A.I.N. study). PAIN Week: American Academy of Neurology 2014 Meeting. 2014, 2014; Las Vegas NV.
36. Dembe A, Wickizer T, Sieck C, Partridge J, et al. Opioid use and dosing in the workers’ compensation setting. A comparative review and new data from Ohio. Am. J. Ind. Med. 2012; 55 (4): 313-324.
37. Mayfield D, Mcleod G, Hall P. The CAGE Questionnaire: Validation of a New Alcoholism Screening Instrument. Am. J. Psychiatry. 1974; 131 (10): 1121-1123.
38. Skinner HA, Holt S, Schuller R, Roy J, et al. Identification of alcohol abuse using laboratory tests and a history of trauma. Ann. Intern. Med. 1984; 101 (6): 847-851.
39. Cyr MG, Wartman SA. The effectiveness of routine screening questions in the detection of alcoholism. JAMA. 1988; 259 (1): 51-54.
40. Butler SF, Budman SH, Fernandez KC, Fanciullo GJ, et al. Cross-Validation of a Screener to Predict Opioid Misuse in Chronic Pain Patients (SOAPP-R). J. Addict. Med. 2009; 3 (2): 66-73.
41. Webster LR, Webster RM. Predicting aberrant behaviors in opioid-treated patients: preliminary validation of the Opioid Risk Tool. Pain Med. 2005; 6 (6): 432-442.
42. Bronstein SR PS, Munitz L, Leider H. Can clinicians accurately predict which patients are misusing their medications? J. Pain. 2011; 12 (4).
43. Brousse G, Vorspan F, Ksouda K, et al. Could the inter-individual variability in cocaine-induced psychotic effects influence the development of cocaine addiction? Towards a new pharmacogenetic approach to addictions. Med. Hypotheses. 2010; 75 (6): 600-604.
44. Hegmann KT, Weiss MS, Bowden K, et al. ACOEM practice guidelines: opioids for treatment of acute, subacute, chronic, and postoperative pain. J. Occup. Environ. Med. 2014; 56 (12): e143-159.
45. Institute WLD. Official Disability Guidelines. Work Loss Data Institute. 2014.
46. Bruehl S, Apkarian AV, Ballantyne JC, et al. Personalized medicine and opioid analgesic prescribing for chronic pain: opportunities and challenges. J. Pain. 2013; 14 (2): 103-113.
47. Loscher W, Schmidt D. Experimental and clinical evidence for loss of effect (tolerance) during prolonged treatment with antiepileptic drugs. Epilepsia. 2006; 47 (8): 1253-1284.
48. Klepstad P, Dale O, Skorpen F, Borchgrevink PC, et al. Genetic variability and clinical efficacy of morphine. Acta. Anaesthesiol. Scand. 2005; 49 (7): 902-908.
49. Lee M, Silverman SM, Hansen H, Patel VB, et al. A comprehensive review of opioid-induced hyperalgesia. Pain physician. 2011; 14 (2): 145-161.
50. Sommer C. Peripheral neuropathies: new recommendations for neuropathic pain pharmacotherapy. Nat. Rev. Neurol. 2015; 11 (5): 250-252.
51. Sagheddu C, Aroni S, De Felice M, et al. Enhanced serotonin and mesolimbic dopamine transmissions in a rat model of neuropathic pain. Neuropharmacology. 2015; 97: 383-393.
52. Siegenthaler A, Schliessbach J, Vuilleumier PH, et al. Linking altered central pain processing and genetic polymorphism to drug efficacy in chronic low back pain. BMC pharmacology & toxicology. 2015; 16: 23.
53. van Hecke O, Kamerman PR, Attal N, et al. Neuropathic pain phenotyping by international consensus (NeuroPPIC) for genetic studies: a NeuPSIG systematic review, Delphi survey, and expert panel recommendations. Pain. 2015; 156 (11): 2337-2353.
54. Outcalt S, Kroenke K, Krebs E, et al. Chronic pain and comorbid mental health conditions: independent associations of posttraumatic stress disorder and depression with pain, disability, and quality of life. J. Behav. Med. 2015; 38 (3): 535-543.
55. Burke ALJ, Mathias JL, Denson LA. Psychological functioning of people living with chronic pain: A meta-analytic review. Br. J. Clin. Psychol. 2015; 54 (3): 345-360.
56. Burns LC, Ritvo SE, Ferguson MK, Clarke H, et al. Pain catastrophizing as a risk factor for chronic pain after total knee arthroplasty: a systematic review. J. Pain Res. 2015; 8: 21-32.
57. Guruprasad S, Shah P. Prevalence of depression, anxiety, and stress in patients with mechanical low back pain. IJTRR. 2015; 4 (4): 67-72.
58. Lerman SF, Rudich Z, Brill S, Shalev H, et al. Longitudinal Associations Between Depression, Anxiety, Pain, and Pain-Related Disability in Chronic Pain Patients. Psychosom Med. 2015; 77 (3): 333-341.
59. Kaiser R, Mooreville M, Kannan K. Psychological Interventions for the Management of Chronic Pain: a Review of Current Evidence. Curr. Pain Headache Rep. 2015; 19 (9): 1-8.
60. Blondell RD, Azadfard M, Wisniewski AM. Pharmacologic therapy for acute pain. Am. Fam. Physician. 2013; 87 (11): 766-772.
61. Pilotto A, Seripa D, Franceschi M, et al. Genetic susceptibility to nonsteroidal anti-inflammatory drug-related gastroduodenal bleeding: role of cytochrome P450 2C9 polymorphisms. Gastroenterology. 2007; 133 (2): 465-471.
62. Manubay JM, Muchow C, Sullivan MA. Prescription Drug Abuse: Epidemiology, Regulatory Issues, Chronic Pain Management with Narcotic Analgesics. Primary Care. 2011; 38 (1): 71.
63. Arteta J, Cobos B, Hu Y, Jordan K, et al. Evaluation of How Depression and Anxiety Mediate the Relationship between Pain Catastrophizing and Prescription Opioid Misuse in a Chronic Pain Population. Pain Med. 2015.
64. Asih S, Mayer TG, Williams M, Choi YH, et al. Does Classification of Chronic Musculoskeletal Disorder Patients into Psychosocial Subgroups Predict Differential Treatment Responsiveness and One-year Outcomes after a Functional Restoration Program? The Clinical Journal of Pain. 2015.
65. Beasley M, Prescott GJ, Scotland G, et al. Patient-reported improvements in health are maintained 2 years after completing a short course of cognitive behaviour therapy, exercise or both treatments for chronic widespread pain: long-term results from the MUSICIAN randomised controlled trial. RMD Open. 2015; 1 (1).
66. Mistry CJ, Bawor M, Desai D, Marsh DC, et al. Genetics of Opioid Dependence: A Review of the Genetic Contribution to Opioid Dependence. Current Psychiatry Reviews. 2014; 10 (2): 156-167.
67. Arthur B. Retrospective analysis of the clinical and economic results of genotyping at-risk patients to guide narcotic detoxification. J. Pain. 2013; 14 (4): S38.
68. Blondell RD, Ashrafioun L, Dambra CM, Foschio EM, et al. A Clinical Trial Comparing Tapering Doses of Buprenorphine with Steady Doses for Chronic Pain and Co-existent Opioid Addiction. J. Addict. Med. 2010; 4 (3): 140-146.
69. Heiwe S, Lonnquist I, Kallmen H. Potential risk factors associated with risk for drop-out and relapse during and following withdrawal of opioid prescription medication. Eur. J. Pain. 2011; 15 (9): 966-970.
70. Vowles KE, Ashworth J. Is opioid withdrawal necessary within comprehensive pain rehabilitation programs? Pain. 2011; 152 (9): 1948-1950.
71. Cowan DT, Wilson-Barnett J, Griffiths P, Vaughan DJ, et al. A randomized, double-blind, placebo-controlled, cross-over pilot study to assess the effects of long-term opioid drug consumption and subsequent abstinence in chronic noncancer pain patients receiving controlled-release morphine. Pain Med. 2005; 6 (2): 113-121.
72. Berna C, Kulich RJ, Rathmell JP. Tapering Long-term Opioid Therapy in Chronic Noncancer Pain. Mayo Clin. Proc. 2015; 90 (6): 828-842.
73. Ngo E, Shah S. Screening and Assessment for Substance Abuse. In: Kaye AD, Vadivelu N, Urman RD, eds. Substance Abuse: Springer New York. 2015; p43-52.
74. Smith HS. Opioid metabolism. Mayo Clin Proc. 2009; 84 (7): 613-624.
75. Pretorius RW, Zurick GM. A systematic approach to identifying drug-seeking patients. Fam. Pract. Manag. 2008; 15 (4): A3-5.
76. Cardoney v. Gunsberger. In: Court NCS, ed2005.
77. Estrada v U.S. In: U.S. Circuit Court of Appeals tC, ed2007.
78. Kranzler HR, Modesto-Lowe V, Van Kirk J. Naltrexone vs. nefazodone for treatment of alcohol dependence. A placebo-controlled trial. Neuropsychopharmacology. 2000; 22 (5): 493-503.
79. Koks S, Vasar E. Deramciclane (Egis). Curr. Opin. Investig. Drugs. 2002; 3 (2): 289-294.
80. Wade AG, Crawford GM, Nemeroff CB, et al. Citalopram plus low-dose pipamperone versus citalopram plus placebo in patients with major depressive disorder: an 8-week, double-blind, randomized study on magnitude and timing of clinical response. Psychol. Med. 2011; 41 (10): 2089-2097.
81. Tassin JP. Uncoupling between noradrenergic and serotonergic neurons as a molecular basis of stable changes in behavior induced by repeated drugs of abuse. Biochem. Pharmacol. 2008; 75 (1): 85-97.
82. Nakamura Y, Tajima K, Kawagoe I, Kanai M, et al. Efficacy of traditional herbal medicine, Yokukansan on patients with neuropathic pain. Masui. 2009; 58 (10): 1248-1255.
83. McHugh RK, Hofmann SG, Asnaani A, Sawyer AT, et al. The serotonin transporter gene and risk for alcohol dependence: a meta-analytic review. Drug Alcohol Depend. 2010; 108 (1-2): 1-6.
84. Vaske J, Newsome J, Wright JP. Interaction of serotonin transporter linked polymorphic region and childhood neglect on criminal behavior and substance use for males and females. Dev. Psychopathol. 2012; 24 (1): 181-193.
85. Nedic G, Nikolac M, Sviglin KN, Muck-Seler D, et al. Association study of a functional catechol-O-methyltransferase (COMT) Val108/158Met polymorphism and suicide attempts in patients with alcohol dependence. Int. J. Neuropsychopharmacol. 2011; 14 (3): 377-388.
86. Kocabas NA. Catechol-O-methyltransferase (COMT) pharmacogenetics in the treatment response phenotypes of major depressive disorder (MDD). CNS Neurol Disord Drug Targets. 2012; 11 (3): 264-272.
87. Jugurnauth SK, Chen CK, Barnes MR, et al. A COMT gene haplotype associated with methamphetamine abuse. Pharmacogenet Genomics. 2011; 21 (11): 731-740.
88. Sagud M, Muck-Seler D, Mihaljevic-Peles A, et al. Catechol-O-methyl transferase and schizophrenia. Psychiatr. Danub. 2010; 22 (2): 270-274.
89. Noble EP. D2 dopamine receptor gene in psychiatric and neurologic disorders and its phenotypes. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2003; 116B (1): 103-125.
90. Nyman ES, Sulkava S, Soronen P, et al. Interaction of early environment, gender and genes of monoamine neurotransmission in the aetiology of depression in a large population-based Finnish birth cohort. BMJ Open. 2011; 1 (1): e000087.
91. Le Foll B, Gallo A, Le Strat Y, Lu L, et al. Genetics of dopamine receptors and drug addiction: a comprehensive review. Behav. Pharmacol. 2009; 20 (1): 1-17.
92. Rybakowski JK, Dmitrzak-Weglarz M, Suwalska A, Leszczynska-Rodziewicz A, et al. Dopamine D1 receptor gene polymorphism is associated with prophylactic lithium response in bipolar disorder. Pharmacopsychiatry. 2009; 42 (1): 20-22.
93. Cao BJ, Rodgers RJ. Dopamine D4 receptor and anxiety: behavioural profiles of clozapine, L-745,870 and L-741,742 in the mouse plus-maze. Eur. J. Pharmacol. 1997; 335 (2-3): 117-125.
94. Navarro JF, Luna G, Garcia F, Pedraza C. Effects of L-741,741, a selective dopamine receptor antagonist, on anxiety tested in the elevated plus-maze in mice. Methods Find Exp. Clin. Pharmacol. 2003; 25 (1): 45-47.
95. Chen TJ, Blum K, Payte JT, et al. Narcotic antagonists in drug dependence: pilot study showing enhancement of compliance with SYN-10, amino-acid precursors and enkephalinase inhibition therapy. Med. Hypotheses. 2004; 63 (3): 538-548.
96. Schroeder JP, Cooper DA, Schank JR, et al. Disulfiram attenuates drug-primed reinstatement of cocaine seeking via inhibition of dopamine beta-hydroxylase. Neuropsychopharmacology. 2010; 35 (12): 2440-2449.
97. Gross NB, Duncker PC, Marshall JF. Striatal dopamine D1 and D2 receptors: widespread influences on methamphetamine-induced dopamine and serotonin neurotoxicity. Synapse. 2011; 65 (11): 1144-1155.
98. Fernandez-Castillo N, Ribases M, Roncero C, Casas M, et al. Association study between the DAT1, DBH and DRD2 genes and cocaine dependence in a Spanish sample. Psychiatr. Genet. 2010; 20 (6): 317-320.
99. Ji N, Shuai L, Chen Y, et al. Dopamine beta-hydroxylase gene associates with stroop color-word task performance in Han Chinese children with attention deficit/hyperactivity disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2011; 156B (6): 730-736.
100. Cubells JF, Sun X, Li W, et al. Linkage analysis of plasma dopamine beta-hydroxylase activity in families of patients with schizophrenia. Hum. Genet. 2011; 130 (5): 635-643.
101. Ella E, Sato N, Nishizawa D, et al. Association between dopamine beta hydroxylase rs5320 polymorphism and smoking behaviour in elderly Japanese. J. Hum. Genet. 2012; 57 (6): 385-390.
102. Peerbooms OL, van Os J, Drukker M, et al. Meta-analysis of MTHFR gene variants in schizophrenia, bipolar disorder and unipolar depressive disorder: evidence for a common genetic vulnerability? Brain Behav. Immun. 2011; 25 (8): 1530-1543.
103. Gelernter J, Gueorguieva R, Kranzler HR, et al. Opioid receptor gene (OPRM1, OPRK1, and OPRD1) variants and response to naltrexone treatment for alcohol dependence: results from the VA Cooperative Study. Alcohol Clin. Exp. Res. 2007; 31 (4): 555-563.
104. Carlezon WA Jr., Beguin C, Knoll AT, Cohen BM. Kappa-opioid ligands in the study and treatment of mood disorders. Pharmacol. Ther. 2009; 123 (3): 334-343.
105. Han DH, Bolo N, Daniels MA, et al. Craving for alcohol and food during treatment for alcohol dependence: modulation by T allele of 1519T>C GABAAalpha6. Alcohol. Clin. Exp. Res. 2008; 32 (9): 1593-1599.
106. Ling W, Shoptaw S, Hillhouse M, et al. Double-blind placebo-controlled evaluation of the PROMETA protocol for methamphetamine dependence. Addiction. 2012; 107 (2): 361-369.
107. Smith HS. The role of genomic oxidative-reductive balance as predictor of complex regional pain syndrome development: a novel theory. Pain physician. 2010; 13 (1): 79-90.
108. Nelson EC, Lynskey MT, Heath AC, et al. Association of OPRD1 polymorphisms with heroin dependence in a large case-control series. Addict. Biol. 2014; 19 (1): 111-121.

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