Molecular Genetics and New Medication Strategies for Opioid Addiction
Abstract
The opioid epidemic is at the epicenter of the drug crisis, resulting in an inconceivable number of overdose deaths and exorbitant associated medical costs that have crippled many communities across the socioeconomic spectrum in the United States. Classic medications for the treatment of opioid use disorder predominantly target the opioid system and thus have been underutilized, in part due to their own potential for abuse and heavy regulatory burden for patients and clinicians. Opioid antagonists are now evolving in their use, not only to prevent acute overdoses but as extended-use treatment options. Strategies that target specific genetic and epigenetic factors, along with novel nonopioid medications, hold promise as future therapeutic interventions for opioid abuse. Success in increasing the treatment options in the clinical toolbox will, hopefully, help to end the historical pattern of recurring opioid epidemics.
[AJP at 175: Remembering Our Past As We Envision Our Future
Drug Addiction in Relation to Problems of Adolescence
Zimmering and colleagues wrote in the midst of an opiate epidemic among young people that ”only the human being, or rather certain types of human beings, will return to the enslaving, self-destructive habit.” (Am J Psychiatry 1952; 109:272–278)]
The scourge of drug abuse has often defined key periods in human history. One hundred seventy-five years ago, when the first issue of what would become the American Journal of Psychiatry appeared, the Opium Wars dominated life in Asia (1, 2). Opium, which initially was imported to China for medicinal purposes, quickly transitioned to a substance of recreational use and addiction that penetrated much of the region, devastating all levels of society. As Chinese emperors attempted to halt the epidemic, Western nations fought the Chinese to increase opium imports and taxes. Another heroin epidemic—this time particularly affecting urban areas of the United States in the 1970s and American veterans of the Vietnam War—served as the major impetus for the creation of the U.S. Drug Enforcement Administration in 1973 (3). Today, the scourge of a new wave of opioid addiction has transected every sociodemographic community in the United States, leading to severe health care and societal burdens of epidemic proportions, with an economic cost of more than $500 billion per year (4).
The opioid tsunami that has gripped the country stemmed in large part from a distorted and biased understanding of addiction vulnerability, fueled by a fervent overprescribing of opioid analgesics that, on an annual basis, exceeded the clinical needs of the entire adult population in the United States. The broad exposure to potent opioids, across the socioeconomic spectrum, led many individuals to use heroin (approximately 80% of new users of heroin started out misusing opioid prescription analgesics) (5–7) or the illegal and less expensive versions of prescription medications, such as fentanyl, for which popularity and illegal sales increased after federal regulations reduced access to legal prescriptions. The consequences have been astonishing, with more than 50,000 overdose deaths yearly (8, 9), a number that is expected to remain in the coming years if drastic interventions are not taken. The devastating impact of the opioid epidemic has had profound medical consequences, with an approximate 3,000% rise in medical services needed for patients with opioid misuse and dependence, as evidenced by a dramatic increase in the number of patients who received medical care from 2007 (approximately 217,000 patients) to 2014 (approximately 7 million patients) (10).
Despite the significant need for therapeutic interventions to meet the urgent demands of the opioid crisis, most of the more than 2.6 million people diagnosed as having opioid use disorder receive minimal treatment for their addiction. The most common pharmacotherapies for opioid use disorder are opioid substitution medications that are associated with marked stigma and strict government regulations due to their abuse liability and potential diversion to the black market. Additionally, these medications require very close clinical monitoring that, altogether, incurs a significant health care burden. Thus, together with the scarcity of clinicians trained in the recognition and treatment of substance use disorders, the ability of health care providers in the present treatment system to service the enormous proportions of people affected by the opioid epidemic is limited. We submit that a multipronged strategy, including a broad repertoire of treatment options based on science-driven approaches, is critical not only to target the current opioid crisis but also to prevent future epidemics. Here, we summarize current therapeutic strategies for opioid misuse and dependence and explore some of the diverse and unique approaches being developed to expand the clinical toolbox for treating opioid use disorder.
Current Strategies of Opioid Treatments
Addiction is a chronic brain disorder that requires long-term treatment. Disturbingly, advertisements that tout expensive addiction “cures” after 30 days in a spa-like residential program with group therapy reflect an abysmal lack of knowledge of the abundant clinical research literature. Persons who enroll in such abstinence-only residential treatment programs, despite the promise of a cure, have very high relapse rates shortly after “graduation” or discharge. Medication-assisted treatment provides the best long-term results, and there are several different medication options for opioid use disorder.
The full agonist approach is represented by methadone. This treatment was developed in the 1960s when it was discovered that patients with opioid addiction could be maintained on a single daily dose of methadone, with reduction in both craving and drug-seeking behavior. More than 50 years of data have demonstrated that patients who receive appropriate methadone treatment plus therapeutic counseling are able to function well in school or employment and maintain a good quality of life. Tolerance develops with use of all opioid agonists, and methadone is no exception. However, because methadone tolerance does not continue to increase, it can be prescribed at the same dosage for many years, although problems can arise when the medication is discontinued, since detoxification can be difficult and can last many months.
The partial agonist approach is represented by buprenorphine. Buprenorphine has a high affinity for the mu opioid receptor but has an upper limit, or “ceiling,” regarding maximal opioid effects. Similar to methadone, buprenorphine blocks craving and drug seeking, but its limited ceiling effect means that patients with a very high pharmacological level of opioid dependence may not be able to be transferred directly to this medication. In the United States, combination treatment—with buprenorphine plus naloxone—is generally used. If the combination is injected, rather than ingested, by the normal oral or sublingual route, naloxone reduces the pleasurable effects of mu opioid receptor stimulation and thus discourages abuse.
A recent treatment innovation is the development of several extended-release injectable formulations. The first treatment that provides slow release of buprenorphine for 30 days was just made available in 2018. Other prospective treatments (lasting as long as 6 months) are currently under review by the Food and Drug Administration (FDA).
The antagonist approach is represented by naltrexone. The oral version of this medication was approved by the FDA in 1985. Naltrexone occupies opioid receptors and prevents agonist drugs, such as heroin and methadone, from binding to the receptors, and as a pure antagonist it does not produce euphoria or reward. The oral version requires daily administration or administration three times weekly. However, patients can relapse simply by discontinuing the medication for 48 hours. Thus, the oral form of naltrexone has had very limited success. More recently, an extended-release version of naltrexone has become available that prevents relapse to opioid addiction for 30 days. Many patients find it convenient to receive a monthly injection rather than take a daily dose. In a 2016 clinical trial, volunteer patients in the probation system who were randomly assigned to 6 months of extended-release naltrexone had significantly more drug-negative urine samples and a lower relapse rate compared with patients who received treatment as usual in the community (11). Nonetheless, antagonist treatments are currently not as widely accepted as other treatments. It is considered challenging to integrate them into the normal opioid agonist treatment regimens, because detoxification of patients is required before an antagonist can be administered. The fact that initial detoxification normally occurs in residential treatment does, however, provide for an important clinical window in which antagonist treatment could be initiated before individuals leave the protected residential environment.
Overall, in looking back over the course of the opioid epidemic, it is evident that there are several challenges pertaining to the use of conventional opioid medications, and these challenges need to be considered in any endeavor to change the trajectory of this crisis. First, very few physicians have been trained in the biology of addiction and the use of opioid medications, and as a result, existing opioid treatments are still not optimally used for treating pain. Second, there continues to be bias toward the use of opioid agonists for initial treatment. Although this is an important option, particularly for persons with opioid use disorder who have been maintained on these agonists for years, individuals who are newly addicted are rarely given the opportunity to be treated with extended-release antagonists that are effective and devoid of addictive properties. Moreover, limited nonopioid strategies exist that physicians can offer their patients.
Looking Forward to Different Approaches
Several new therapeutic strategies currently being explored may help expand traditional ways of thinking and eventually lead to acceleration of the development of effective interventions.
Genetic Strategies in Opioid Treatment—Pharmacogenomics
Individual differences in genetics play an important role in a person’s vulnerability to developing opioid use disorder. It is estimated, on the basis of twin studies, that approximately 50% of the variation in opioid addiction is attributable to genetic factors (12, 13). Although an individual’s genetic makeup is not deterministic in the development of a substance use disorder, especially if a person is never exposed to an agent, knowledge of genetic vulnerability can help provide important insight pertaining to the underlying neurobiology of substance use disorders, reveal novel biological targets for potential therapeutic development, and, potentially, optimize personalized medication therapy. The OPRM1 gene on chromosome 6 that encodes the mu opioid receptor has logically been a high-priority candidate in studies investigating disease risk and pharmacogenomic factors associated with opioid use. The locus of the OPRM1 gene that has received the most attention is the common missense single-nucleotide polymorphism (SNP) A118G rs1799971, a nonsynonymous point mutation that changes the amino acid sequence of the protein (14). The OPRM1 variants have been shown to have functional relevance with regard to in vitro mu opioid receptor binding and signaling (15–17), in vivo mu opioid receptor binding (18, 19), mu opioid receptor signaling in human postmortem specimens (20–22), and opioid neuropeptide gene expression levels relevant to addiction in the human brain (23). Most of the findings suggest reduced mu opioid receptors in subjects with the A118G SNP. Other OPRM1 variants have been investigated with regard to heroin addiction (24–27) and the functional relationship to mu opioid receptor signaling and downstream transcriptional regulation (21).
Multiple studies have addressed the relationship of the rs1799971 polymorphism to heroin and opioid abuse (15, 23, 28–30). Not surprisingly, to date, results for OPRM1 from candidate gene studies have been equivocal, due in part to low sample sizes as well as differences in the race and ethnicity of the study subjects or differences in potential phenotype and environmental variables, among other factors. Meta-analyses of opioid use disorder that attempted to increase statistical power by combining the results from multiple investigations have also been inconclusive (31, 32) but suggest a contribution to addiction liability shared across different addictive substances (33). Additionally, there is research implicating the rs1799971 allele in naltrexone response in the treatment of alcohol use disorder (34, 35). Given the multifaceted nature of addiction, it is evident that a single-gene focus is an extremely limited strategy for demonstrating conclusive genetic contributions. Indeed, a large, comprehensive replication study demonstrated that the rs1799971 SNP was only associated with heroin addiction in the presence of another SNP (rs3778150), which was identified as a disease-associated expression quantitative trait locus that influenced OPRM1 expression in the human prefrontal cortex (26). These findings may explain some of the discrepant literature regarding the association between the rs1799971 genotype and heroin and opioid addiction, and they highlight the importance of haplotype strategies for complex disorders such as addiction, in which the combination of alleles that are inherited together has stronger statistical power in associating a genetic link with the phenotype.
An important question for guiding future clinical care is whether documented functional differences of OPRM1 variants could be leveraged to improve the pharmacological response in patients undergoing opioid treatment (e.g., methadone) and to prevent adverse effects, including addiction vulnerability in healthy individuals who are prescribed opioid analgesics. Determining the effective individual dosage for methadone is often clinically challenging, because underdosing can lead to craving and relapse, and high doses can induce euphoria and sedation as well as other side effects. Implementing an agnostic genome-wide association study approach, Smith et al. (36) recently identified one statistically significant region in the genome that was associated with higher daily methadone dosing in African American (but not Caucasian) patients with opioid dependence. Interestingly, the region was on chromosome 6, with the lead SNP rs73568641 localized in the OPRM1 gene. The authors replicated the finding showing the SNP to be associated with increased morphine dosage requirements for pain relief in an independent sample of African American children treated for surgical pain. Significant research is needed to determine whether the rs73568641 SNP has a causal relationship to the expression or function of the mu opioid receptor. Nevertheless, these findings represent a critical step forward, suggesting that OPRM1 genetics could be useful clinically in determining appropriate opioid medication dosages. Results from a meta-analysis (37) and other studies (38, 39) also suggest that the A118G rs1799971 allele variant can influence opioid pain management in individuals carrying the A118G rs1799971 allele who require higher opioid doses than A118A carriers. The fact that OPRM1 may hold promise as a genetic predictor of opioid medication dosage in the setting of addiction treatment and in analgesia could be helpful in identifying drug-naive individuals without dependence who may have a potential risk for addiction when treated with opioid prescription medications. However, large-scale investigations are needed before individual OPRM1 genetics can be incorporated into the clinical formula for setting optimal opioid treatment dosages for opioid use disorder and pain management.
Additionally, it is important to reemphasize that it is unlikely that only the OPRM1 gene will be able to inform and improve clinically relevant treatment on the basis of genetics. Functional genetic variations of other genes, such as those involved with liver metabolic enzyme activity, were recently reported to be associated with the steady-state plasma concentration of methadone enantiomers, which provide a measure of methadone metabolism and are used clinically as an index of treatment response and efficacy of methadone therapy (40, 41). If replicated, such strategies will help to individualize treatment to achieve dosage optimization for patients with opioid use disorder, to reduce and avert the onset of withdrawal symptoms, and to optimize opioid pain management for persons without dependence.
Alternative Splicing to Guide Targeted Opioid Medications
DNA sequence variations and the mechanism of their regulation of gene expression and disease phenotype are complex and not well understood; however, multiple processes have begun to be explored as potential targets for medication development. Alternative splicing of genes is an efficient means of generating variation in protein function and thus has been of growing interest in attempts to personalize and optimize pharmacological therapies. Splicing determines which exons of a gene that code for its amino acid product (e.g., the mu opioid receptor) are used or not used to synthesize the final receptor. As a result, there can be multiple subtypes of the mu receptor, based on differences in splicing. Not surprisingly, the development of novel medications based on molecular genetics has involved consideration of the multiple isoforms of the mu opioid receptor. An array of mu opioid receptor variants is produced by alternative pre-mRNA splicing of the single copy of the OPRM1 gene (42, 43). The extensive alternative splicing of OPRM1 creates at least three structurally distinct classes of splice variants that are conserved from rodent to human, thus improving the possibility for preclinical scientific studies to better inform human investigations. Animal studies have shown, for example, that the different truncated variants at the C-termini generated from 3′-alternative splicing of the OPRM1 gene do not substantially affect morphine analgesia but differentially alter morphine-induced tolerance, physical dependence, and reward behavior (44). Additionally, whereas normal analgesia is maintained for morphine and methadone analgesia in variants within exon 11 of the OPRM1 gene, the analgesic actions of heroin and fentanyl are markedly decreased (45). Thus, developing opioid analgesics that lack the side effects of traditional opioids may be possible by targeting truncated splice variants of the mu opioid receptor (46, 47). Altogether, research efforts to dissociate the desirable analgesic properties of opioids from the undesirable side effects of addiction may be possible. Targeting specific regions of the mu opioid receptor could be an effective therapeutic strategy to reduce the abuse and addiction liability of opioids while maintaining analgesic properties.
The recent selective molecular targeting of the mu opioid receptor through biased agonism, although not a genetic approach, represents a significant advancement in the ability to selectivity target specific downstream signal transduction pathways in the same G-protein-coupled receptor for medication development (48–50). In contrast to the classic categorization of ligands as full, partial, or inverse agonists or antagonists, biased agonism leverages the capability of G-protein–coupled receptors to stabilize receptor conformation to regulate different signaling pathways. Agonists have thus been designed to deliver different physiologic outcomes by biasing a selective downstream signal transduction pathway (such as G-protein signaling, beta-arrestin recruitment, and receptor internalization) mediated by the same receptor. This strategy significantly expands the repertoire for drug discovery for ligands targeting mu opioid receptor signaling to potentially have analgesic properties (such as those recruiting beta-arrestin proteins) while avoiding tolerance or other opioid adverse effects (linked to G-protein signaling) (51, 52). Clearly, the fact that individual variation exists for genes aligned to distinct G-protein–coupled receptor pathways indicates that genetic factors could dictate which individuals might respond to certain biased agonists.
Epigenetics-Informed Opioid Treatment
In addition to genetics, susceptibility to opioid addiction is known to be strongly influenced by environmental factors. Thus, epigenetics (biological mechanisms that mediate genetic control of gene expression without a change in DNA sequence) could be of significant importance for understanding individual vulnerability to addiction and response to treatment. The epigenetic mechanisms that turn genes on and off to set the state of gene expression patterns and thus cellular function include methylation of DNA and modifications (e.g., methylation, acetylation, and phosphorylation) of histones. Epigenetics has emerged as an important biological driver of addiction pathology (53–56). To date, most epigenetic studies that are relevant to opioid use disorder have focused on DNA methylation. A number of investigations have reproducibly observed that chronic exposure to opioids (e.g., in patients with chronic pain being treated with opioids, active heroin users, and former heroin users receiving methadone maintenance treatment) induces epigenetic changes in peripheral marks (lymphocyte and blood), including increased methylation of the OPRM1 gene (57, 59–61). The hypermethylation of DNA located in the OPRM1 promoter appears to block the binding of transcription activators such as Sp1, which ultimately leads to silencing of the OPRM1 (62). Reduced mu opioid receptor expression that has been detected in various brain regions in individuals who abuse heroin (21, 63, 64) may relate to their increased opioid requirement. Consistent with this, pain relief in patients with cancer has been shown to correlate with methylation of the OPRM1 promoter, with high-dose opioid use associated with OPRM1 hypermethylation (57). These and other studies suggest that DNA methylation in peripheral blood samples, and thus a potential proxy for CNS mu opioid receptor function, could provide a biomarker for OPRM1 function that could aid in determining dosage. However, it is important to emphasize the cell-specific nature of epigenetic mechanisms where clear DNA methylation differences have, for example, been shown in different neuronal and glial populations in the prefrontal cortex in heroin users (65), and thus what specific CNS function that any alterations of peripheral OPRM1 methylation would predict remains unclear. Furthermore, although the OPRM1 is a rational target for research in guiding future clinical care, the gene list needs to be expanded by gathering genome-wide unbiased data from large-scale clinical studies to more efficiently direct pharmacoepigenetic approaches.
A critical aspect of epigenetics that makes it an intriguing strategic therapeutic target is that the modifications are reversible. Moreover, multiple families of proteins are involved in adding (writers), recognizing (readers), or removing (erasers) epigenetic marks (66, 67). This plethora of proteins provides a diverse system to tweak the tone of gene expression and thus cellular functions and phenotypes relevant to addiction. The importance of epigenetics to opioid use disorder was highlighted in a recent postmortem study of the human striatum in persons who abused heroin (53). Epigenetic disturbances were observed to correlate with alterations of genes relevant to glutamatergic function and synaptic plasticity, impairments of which are well acknowledged as a hallmark of addiction pathology (68, 69). Interestingly, enhanced histone acetylation levels (and specifically acetylation of the histone H3 protein, lysine 27) in the striatum of persons who abused heroin correlated significantly with years of heroin use. It is well known that acetylated lysine residues on histones are specifically recognized and “read” by the bromodomains and extraterminal subfamily of proteins. Bromodomains and extraterminal inhibitors have become a favored strategy, developed as anticancer medications that could provide novel agents to repurpose as potential treatments for opioid use disorder (53). A small molecular bromodomain and extraterminal inhibitor, JQ-1, reduced heroin self-administration and heroin-seeking behavior in a rodent model, thus setting the stage for these inhibitors to be investigated in clinical trials of persons with opioid use disorder. The wide range of epigenetic molecules being developed for many clinical symptoms and diseases opens a treasure trove of compounds that could be examined in relation to epigenetic pathologies in addiction.
Medical Cannabinoids—Cannabidiol
Recent attention has focused on “medical marijuana” as a potential nonconventional strategy. Several recent epidemiological studies, although still in their infancy in data collection, suggest that in states with existing medical marijuana laws, there has been a reduction in opioid-related deaths, opioid prescriptions, and opioid-related motor vehicle fatalities (70–74). Many factors—even those unrelated to the pharmacological effects of cannabis on brain function relevant to opioid use—may account for the apparent associations. However, broad use of the term “medical marijuana” (often confused with conventional recreational marijuana) clearly ignores the complex nature of the plant, which contains hundreds of cannabinoids and other entourage compounds that are essential to consider in the development of a clinically useful medication. What is known from a number of preclinical studies is that different cannabinoids can have adverse or beneficial effects on opioid sensitivity. For example, whereas tetrahydrocannabinol, the psychoactive component of cannabis, can enhance reward sensitivity to opioids (75–78), exposure to cannabidiol, a nonrewarding cannabinoid, reduces the reward-facilitating effect of morphine (79) and reduces cue-induced heroin-seeking behavior, even weeks after the last cannabidiol exposure (80). Cannabidiol normalizes glutamatergic impairments induced by heroin self-administration (80). Such findings have set in motion many research studies examining not only opioids but other drugs of abuse in relation to the potential impact of cannabidiol. Moreover, results from pilot clinical studies have suggested replication of animal findings of cannabidiol reducing cue-induced cravings, as well as anxiety, in individuals who are abstinent from heroin use (81). Intriguingly, similar to the rodent model, cannabidiol resulted in a maintained reduction in heroin craving even a week after the last administration. Cannabidiol’s protracted action may be of particular benefit in a successful therapeutic strategy for opioid use disorder, because the protective effects in reducing craving, and thus risk of relapse, could be maintained even if the individual has missed a daily dose. Importantly, cannabidiol lacks any rewarding effects (79, 82–85) and has a wide safety margin (86–88), and thus would not require the restrictive governmental regulations associated with opioid agonist medications that have abuse potential and are diverted to the black market. However, cannabidiol is still currently under the cannabis umbrella of a schedule I drug. As additional clinical trials are conducted, the knowledge gained will, hopefully, help revise the federal regulations so that a full battery of research can be explored to determine the potential of cannabidiol for opioid use disorder treatment. As with all novel strategies, future application of cannabidiol for opioid use disorder should determine what specific aspect of the complex clinical spectrum of the disorder (e.g., craving, acute reward substitution) would most optimally be targeted by this approach.
Vaccines
Although not new, another “outside the box” approach, originally considered nearly 40 years ago, involves the development of antidrug vaccines. The first vaccine was designed to target opioids (89). Vaccines for the treatment of other drugs of abuse, such as nicotine (90–92) and cocaine (93–96), have been tested to a greater extent in human subjects, with mixed results, seeming to depend on individual variability in antibody titer levels. The challenge of raising sufficiently high antibody titers has been recently addressed with a novel strategy to develop a more efficacious heroin conjugate vaccine in combination with specific carrier proteins and adjuvants (97, 98). This antiheroin vaccine approach was recently evaluated in preclinical models (with mice and nonhuman primates) and resulted in a significant 15-fold reduction of heroin operant responding for 8 months in nonhuman primates (99). Future clinical studies will help to determine whether the promise of antiheroin vaccines can indeed achieve their long promise.
The Roadmap Forward
We cannot address the current opioid epidemic with old tools, including declarations of an opioid “war” and harsh judicial ramifications, as previously employed over the past century. These approaches failed in the past, and they exacerbated psychosocial pathologies that persist today. Instead, it is essential that the education of prescribing physicians and of the general public about the benefits and dangers of opioids be complemented with knowledge of the rapid development and translation of novel strategies to expand currently available medications. To meet an epidemic, a different mentality needs to be employed in which specific paths are created at the level of the federal and state governments to mobilize the efforts of scientists and clinicians to advance care, prevention, and ultimately treatments. Strategies should span the improvement of current opioid treatments by leveraging genetic and epigenetic factors as well as the development of new therapies, such as medical cannabinoids and innovative medications that could specifically strengthen impaired synaptic plasticity in the management of opioid use disorder. These approaches might also be employed to reduce the transition to addiction in patients without dependence who are treated with opioids for chronic pain. Moreover, updating medical school curricula with information regarding evidence-based treatments for pain and opioid use vulnerability would be beneficial.
What continues to be missing in the development of novel medications, especially in consideration of personalized medicine and the complex nature of addiction disorders, is the structured clinical phenotyping of patients that is critical to integrate with genetic and epigenetic data. Such knowledge can provide a strong biological foundation on which to develop better targeted personalized medication strategies. Nevertheless, irrespective of developing the most effective innovative medication for opioid use disorder, supportive social services must go hand in hand with drug development. There will not be a miracle therapeutic strategy. The science-based future medication approaches discussed above and elsewhere are interesting, but even the most promising strategies will fail to be realized without fast-track transition of preclinical and early-stage phase 1 clinical studies to full clinical trials and an “all hands on board” approach that involves input from patients and their families. There is much to be learned after 175 years that will help transform the clinical toolbox in the coming years.
1 : The Opium War: Drugs, Dreams, and the Making of Modern China. New York, Overlook Press, 2015Google Scholar
2 : The Opium War, 1840–1842: Barbarians in the Celestial Empire in the Early Part of the Nineteenth Century and the War by Which They Forced Her Gates. Chapel Hill, NC, University of North Carolina Press, 1998Google Scholar
3 : The Drug Enforcement Administration (DEA) Years: 1970–1975. Springfield, Va, Drug Enforcement Administration. https://www.dea.gov/about/history.shtmlGoogle Scholar
4 : The Underestimated Cost of the Opioid Crisis. Washington, DC, Executive Office of the President, 2017Google Scholar
5 : Associations of nonmedical pain reliever use and initiation of heroin use in the United States. Rockville, Md, Substance Abuse and Mental Health Services Administration. http://archive.samhsa.gov/data/2k13/DataReview/DR006/nonmedical-pain-reliever-use-2013.pdf2013Google Scholar
6 : Non-medical use of prescription opioids is associated with heroin initiation among US veterans: a prospective cohort study. Addiction 2016; 111:2021–2031Crossref, Medline, Google Scholar
7 : Relationship between nonmedical prescription-opioid use and heroin use. N Engl J Med 2016; 374:154–163Crossref, Medline, Google Scholar
8 : Provisional Counts of Drug Overdose Death (as of Aug 6, 2017). Atlanta, National Center for Health Statistics, National Vital Statistics System, Centers for Disease Control and Prevention, 2017Google Scholar
9 : Provisional Drug Overdose Death Counts. Atlanta, National Center for Health Statistics, Centers for Disease Control and Prevention, 2018Google Scholar
10 FAIR Health: The impact of the Opioid Crisis on the Healthcare System: A Study of Privately Billed Services. New York, FAIR Health, 2016Google Scholar
11 : Extended-release naltrexone opioid treatment at jail reentry (XOR). Contemp Clin Trials 2016; 49:57–64Crossref, Medline, Google Scholar
12 : Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry 1998; 55:967–972Crossref, Medline, Google Scholar
13 : Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry 2003; 160:687–695Link, Google Scholar
14 : A common single nucleotide polymorphism A118G of the μ opioid receptor alters its N-glycosylation and protein stability. Biochem J 2012; 441:379–386Crossref, Medline, Google Scholar
15 : Single-nucleotide polymorphism in the human mu opioid receptor gene alters beta-endorphin binding and activity: possible implications for opiate addiction. Proc Natl Acad Sci USA 1998; 95:9608–9613Crossref, Medline, Google Scholar
16 : Single-nucleotide polymorphism (A118G) in exon 1 of OPRM1 gene causes alteration in downstream signaling by mu-opioid receptor and may contribute to the genetic risk for addiction. J Neurochem 2010; 112:486–496Crossref, Medline, Google Scholar
17 : A single nucleotide polymorphic mutation in the human mu-opioid receptor severely impairs receptor signaling. J Biol Chem 2001; 276:3130–3137Crossref, Medline, Google Scholar
18 : Human mu opioid receptor (OPRM1 A118G) polymorphism is associated with brain mu-opioid receptor binding potential in smokers. Proc Natl Acad Sci USA 2011; 108:9268–9273Crossref, Medline, Google Scholar
19 : Influence of OPRM1 Asn40Asp variant (A118G) on [11C]carfentanil binding potential: preliminary findings in human subjects. Int J Neuropsychopharmacol 2013; 16:47–53Crossref, Medline, Google Scholar
20 : Allelic expression imbalance of human mu opioid receptor (OPRM1) caused by variant A118G. J Biol Chem 2005; 280:32618–32624Crossref, Medline, Google Scholar
21 : ELK1 transcription factor linked to dysregulated striatal mu opioid receptor signaling network and OPRM1 polymorphism in human heroin abusers. Biol Psychiatry 2013; 74:511–519Crossref, Medline, Google Scholar
22 : Genetic-epigenetic interaction modulates μ-opioid receptor regulation. Hum Mol Genet 2012; 21:4751–4760Crossref, Medline, Google Scholar
23 : Mu opioid receptor A118G polymorphism in association with striatal opioid neuropeptide gene expression in heroin abusers. Proc Natl Acad Sci USA 2006; 103:7883–7888Crossref, Medline, Google Scholar
24 : Low frequency genetic variants in the μ-opioid receptor (OPRM1) affect risk for addiction to heroin and cocaine. Neurosci Lett 2013; 542:71–75Crossref, Medline, Google Scholar
25 : Genetic susceptibility to heroin addiction: a candidate gene association study. Genes Brain Behav 2008; 7:720–729Crossref, Medline, Google Scholar
26 : Cis-expression quantitative trait loci mapping reveals replicable associations with heroin addiction in OPRM1. Biol Psychiatry 2015; 78:474–484Crossref, Medline, Google Scholar
27 : Genotype patterns that contribute to increased risk for or protection from developing heroin addiction. Mol Psychiatry 2008; 13:417–428Crossref, Medline, Google Scholar
28 : Substantial attributable risk related to a functional mu-opioid receptor gene polymorphism in association with heroin addiction in central Sweden. Mol Psychiatry 2004; 9:547–549Crossref, Medline, Google Scholar
29 : No evidence of association between 118A>G OPRM1 polymorphism and heroin dependence in a large Bulgarian case-control sample. Drug Alcohol Depend 2011; 117:62–65Crossref, Medline, Google Scholar
30 : Functional mu opioid receptor polymorphism (OPRM1 A(118) G) associated with heroin use outcomes in Caucasian males: a pilot study. Am J Addict 2015; 24:329–335Crossref, Medline, Google Scholar
31 : Lack of association between the A118G polymorphism of the mu opioid receptor gene (OPRM1) and opioid dependence: a meta-analysis. Pharm Genomics Pers Med 2009; 2:9–19Medline, Google Scholar
32 : OPRM1 rs1799971 polymorphism and opioid dependence: evidence from a meta-analysis. Pharmacogenomics 2013; 14:813–824Crossref, Medline, Google Scholar
33 : Association of the OPRM1 variant rs1799971 (A118G) with non-specific liability to substance dependence in a collaborative de novo meta-analysis of European-ancestry cohorts. Behav Genet 2016; 46:151–169Crossref, Medline, Google Scholar
34 : An evaluation of mu-opioid receptor (OPRM1) as a predictor of naltrexone response in the treatment of alcohol dependence: results from the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study. Arch Gen Psychiatry 2008; 65:135–144Crossref, Medline, Google Scholar
35 : A functional polymorphism of the mu-opioid receptor gene is associated with naltrexone response in alcohol-dependent patients. Neuropsychopharmacology 2003; 28:1546–1552Crossref, Medline, Google Scholar
36 : Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1. Mol Psychiatry 2017; 22:346–352Crossref, Medline, Google Scholar
37 : OPRM1 A118G gene variant and postoperative opioid requirement: a systematic review and meta-analysis. Anesthesiology 2014; 121:825–834Crossref, Medline, Google Scholar
38 : OPRM1 c.118A>G polymorphism and duration of morphine treatment associated with morphine doses and quality-of-life in palliative cancer pain settings. Int J Mol Sci 2017; 18:18Crossref, Google Scholar
39 : How much oxycodone is needed for adequate analgesia after breast cancer surgery: effect of the OPRM1 118A>G polymorphism. J Pain 2014; 15:1248–1256Crossref, Medline, Google Scholar
40 : Methadone dose in heroin-dependent patients: role of clinical factors, comedications, genetic polymorphisms and enzyme activity. Br J Clin Pharmacol 2015; 79:967–977Crossref, Medline, Google Scholar
41 : Effects of cytochrome P450 single nucleotide polymorphisms on methadone metabolism and pharmacodynamics. Biochem Pharmacol (Epub ahead of print, Feb 16, 2018)Google Scholar
42 : Diversity and complexity of the mu opioid receptor gene: alternative pre-mRNA splicing and promoters. DNA Cell Biol 2005; 24:736–750Crossref, Medline, Google Scholar
43 : A heroin addiction severity-associated intronic single nucleotide polymorphism modulates alternative pre-mRNA splicing of the μ opioid receptor gene OPRM1 via hnRNPH interactions. J Neurosci 2014; 34:11048–11066Crossref, Medline, Google Scholar
44 : Alternatively spliced mu opioid receptor C termini impact the diverse actions of morphine. J Clin Invest 2017; 127:1561–1573Crossref, Medline, Google Scholar
45 : Involvement of exon 11-associated variants of the mu opioid receptor MOR-1 in heroin, but not morphine, actions. Proc Natl Acad Sci USA 2009; 106:4917–4922Crossref, Medline, Google Scholar
46 : Truncated G protein-coupled mu opioid receptor MOR-1 splice variants are targets for highly potent opioid analgesics lacking side effects. Proc Natl Acad Sci USA 2011; 108:19778–19783Crossref, Medline, Google Scholar
47 : Mediation of opioid analgesia by a truncated 6-transmembrane GPCR. J Clin Invest 2015; 125:2626–2630Crossref, Medline, Google Scholar
48 : Biased mu-opioid receptor ligands: a promising new generation of pain therapeutics. Curr Opin Pharmacol 2017; 32:77–84Crossref, Medline, Google Scholar
49 : Biased ligand quantification in drug discovery: from theory to high throughput screening to identify new biased μ opioid receptor agonists. Br J Pharmacol 2016; 173:1393–1403Crossref, Medline, Google Scholar
50 : Biased ligands at G-protein-coupled receptors: promise and progress. Trends Pharmacol Sci 2014; 35:308–316Crossref, Medline, Google Scholar
51 : Biased agonism of the μ-opioid receptor by TRV130 increases analgesia and reduces on-target adverse effects versus morphine: a randomized, double-blind, placebo-controlled, crossover study in healthy volunteers. Pain 2014; 155:1829–1835Crossref, Medline, Google Scholar
52 : Bias factor and therapeutic window correlate to predict safer opioid analgesics. Cell 2017; 171:1165-1175Crossref, Medline, Google Scholar
53 : Striatal H3K27 acetylation linked to glutamatergic gene dysregulation in human heroin abusers holds promise as therapeutic target. Biol Psychiatry 2017; 81:585–594Crossref, Medline, Google Scholar
54 : Epigenetic mechanisms of drug addiction. Neuropharmacology 2014; 76:259–268Crossref, Medline, Google Scholar
55 : DNA methylation dynamics and cocaine in the brain: progress and prospects. Genes (Basel) (Epub ahead of print, May 12, 2017)Google Scholar
56 : Emerging role of epigenetic mechanisms in alcohol addiction. Alcohol Clin Exp Res 2017; 41:666–680Crossref, Medline, Google Scholar
57 : OPRM1 methylation contributes to opioid tolerance in cancer patients. J Pain 2017; 18:1046–1059Crossref, Medline, Google Scholar
58 : Elevated levels of DNA methylation at the OPRM1 promoter region in men with opioid use disorder. Am J Drug Alcohol Abuse 2018; 44:193–199Crossref, Medline, Google Scholar
59 : Chronic opioid use is associated with increased DNA methylation correlating with increased clinical pain. Pain 2013; 154:15–23Crossref, Medline, Google Scholar
60 : Elevated levels of DNA methylation at the OPRM1 promoter in blood and sperm from male opioid addicts. J Opioid Manag 2011; 7:258–264Crossref, Medline, Google Scholar
61 : Increased OPRM1 DNA methylation in lymphocytes of methadone-maintained former heroin addicts. Neuropsychopharmacology 2009; 34:867–873Crossref, Medline, Google Scholar
62 : Up-regulation of the mu-opioid receptor gene is mediated through chromatin remodeling and transcriptional factors in differentiated neuronal cells. Mol Pharmacol 2010; 78:58–68Crossref, Medline, Google Scholar
63 : A common human micro-opioid receptor genetic variant diminishes the receptor signaling efficacy in brain regions processing the sensory information of pain. J Biol Chem 2009; 284:6530–6535Crossref, Medline, Google Scholar
64 : Decreased immunodensities of micro-opioid receptors, receptor kinases GRK 2/6 and beta-arrestin-2 in postmortem brains of opiate addicts. Brain Res Mol Brain Res 2004; 121:114–122Crossref, Medline, Google Scholar
65 : DNA methylation profiling of human prefrontal cortex neurons in heroin users shows significant difference between genomic contexts of hyper- and hypomethylation and a younger epigenetic age. Genes (Basel) (Epub ahead of print, May 30, 2017)Google Scholar
66 : The molecular hallmarks of epigenetic control. Nat Rev Genet 2016; 17:487–500Crossref, Medline, Google Scholar
67 : Epigenetic protein families: a new frontier for drug discovery. Nat Rev Drug Discov 2012; 11:384–400Crossref, Medline, Google Scholar
68 : Corticostriatal plasticity, neuronal ensembles, and regulation of drug-seeking behavior. Prog Brain Res 2017; 235:93–112Crossref, Medline, Google Scholar
69 : The glutamate homeostasis hypothesis of addiction. Nat Rev Neurosci 2009; 10:561–572Crossref, Medline, Google Scholar
70 : Medical marijuana laws: the authors reply. Health Aff 2016; 35:1937Crossref, Google Scholar
71 : State medical marijuana laws and the prevalence of opioids detected among fatally injured drivers. Am J Public Health 2016; 106:2032–2037Crossref, Medline, Google Scholar
72 : US traffic fatalities, 1985–2014, and their relationship to medical marijuana laws. Am J Public Health 2017; 107:336–342Crossref, Medline, Google Scholar
73 : Do medical marijuana laws reduce addictions and deaths related to pain killers? J Health Econ 2018; 58:29–42Crossref, Medline, Google Scholar
74 : Medical marijuana policies and hospitalizations related to marijuana and opioid pain reliever. Drug Alcohol Depend 2017; 173:144–150Crossref, Medline, Google Scholar
75 : Chronic THC during adolescence increases the vulnerability to stress-induced relapse to heroin seeking in adult rats. Eur Neuropsychopharmacol 2014; 24:1037–1045Crossref, Medline, Google Scholar
76 : Strain dependence of adolescent cannabis influence on heroin reward and mesolimbic dopamine transmission in adult Lewis and Fischer 344 rats. Addict Biol 2015; 20:132–142Crossref, Medline, Google Scholar
77 : Proenkephalin mediates the enduring effects of adolescent cannabis exposure associated with adult opiate vulnerability. Biol Psychiatry 2012; 72:803–810Crossref, Medline, Google Scholar
78 : Adolescent cannabis exposure alters opiate intake and opioid limbic neuronal populations in adult rats. Neuropsychopharmacology 2007; 32:607–615Crossref, Medline, Google Scholar
79 : Cannabidiol inhibits the reward-facilitating effect of morphine: involvement of 5-HT1A receptors in the dorsal raphe nucleus. Addict Biol 2013; 18:286–296Crossref, Medline, Google Scholar
80 : Cannabidiol, a nonpsychotropic component of cannabis, inhibits cue-induced heroin seeking and normalizes discrete mesolimbic neuronal disturbances. J Neurosci 2009; 29:14764–14769Crossref, Medline, Google Scholar
81 : Early phase in the development of cannabidiol as a treatment for addiction: opioid relapse takes initial center stage. Neurotherapeutics 2015; 12:807–815Crossref, Medline, Google Scholar
82 : Effect of low doses of delta9-tetrahydrocannabinol and cannabidiol on the extinction of cocaine-induced and amphetamine-induced conditioned place preference learning in rats. Psychopharmacology (Berl) 2004; 175:360–366Crossref, Medline, Google Scholar
83 : Divergent effects of cannabidiol on the discriminative stimulus and place conditioning effects of delta(9)-tetrahydrocannabinol. Drug Alcohol Depend 2008; 94:191–198Crossref, Medline, Google Scholar
84 : Oral cannabidiol does not produce a signal for abuse liability in frequent marijuana smokers. Drug Alcohol Depend 2017; 172:9–13Crossref, Medline, Google Scholar
85 : Oral cannabidiol does not alter the subjective, reinforcing or cardiovascular effects of smoked cannabis. Neuropsychopharmacology 2016; 41:1974–1982Crossref, Medline, Google Scholar
86 : Safety and pharmacokinetics of oral cannabidiol when administered concomitantly with intravenous fentanyl in humans. J Addict Med 2015; 9:204–210Crossref, Medline, Google Scholar
87 : Cannabidiol (CBD) as an adjunctive therapy in schizophrenia: a multicenter randomized controlled trial. Am J Psychiatry 2018; 175:225–231Link, Google Scholar
88 : An update on safety and side effects of cannabidiol: a review of clinical data and relevant animal studies. Cannabis Cannabinoid Res 2017; 2:139–154Crossref, Medline, Google Scholar
89 : Changes in heroin self-administration by a rhesus monkey after morphine immunisation. Nature 1974; 252:708–710Crossref, Medline, Google Scholar
90 : Immunogenicity and smoking-cessation outcomes for a novel nicotine immunotherapeutic. Clin Pharmacol Ther 2011; 89:392–399Crossref, Medline, Google Scholar
91 : The immunogenicity and safety of a nicotine vaccine in smokers and nonsmokers: results of a randomized, placebo-controlled phase 1/2 trial. Nicotine Tob Res 2008; 10:213–218Crossref, Medline, Google Scholar
92 : Nicotine vaccines for smoking cessation. Cochrane Database Syst Rev 2012; 8:CD007072Medline, Google Scholar
93 : Cocaine-specific antibodies blunt the subjective effects of smoked cocaine in humans. Biol Psychiatry 2010; 67:59–65Crossref, Medline, Google Scholar
94 : Cocaine vaccine for the treatment of cocaine dependence in methadone-maintained patients: a randomized, double-blind, placebo-controlled efficacy trial. Arch Gen Psychiatry 2009; 66:1116–1123Crossref, Medline, Google Scholar
95 : Human therapeutic cocaine vaccine: safety and immunogenicity. Vaccine 2002; 20:1196–1204Crossref, Medline, Google Scholar
96 : Vaccine for cocaine dependence: a randomized double-blind placebo-controlled efficacy trial. Drug Alcohol Depend 2014; 140:42–47Crossref, Medline, Google Scholar
97 : Enhancing efficacy and stability of an antiheroin vaccine: examination of antinociception, opioid binding profile, and lethality. Mol Pharm 2018; 15:1062–1072Crossref, Medline, Google Scholar
98 : Conjugate vaccine immunotherapy for substance use disorder. Pharmacol Rev 2017; 69:298–315Crossref, Medline, Google Scholar
99 : Development of a clinically viable heroin vaccine. J Am Chem Soc 2017; 139:8601–8611Crossref, Medline, Google Scholar