GENETIC FACTORS INVOLVED IN SUBSTANCE USE DISORDERS WITH REVIEW OF THE RISING OPIOID EPIDEMIC
by
Alec John Howard
B.A., Saint Vincent College, 2013
Submitted to the Graduate Faculty of
Human Genetics
Graduate School of Public Health in partial fulfillment
of the requirements for the degree of
Master of Public Health
University of Pittsburgh
2016
ii
UNIVERSITY OF PITTSBURGH
GRADUATE SCHOOL OF PUBLIC HEALTH
This essay is submitted
by
Alec J. Howard
on
December 16, 2016
Essay Advisor:Candace Kammerer, PhD ______________________________________Associate ProfessorDepartment of Human GeneticsGraduate School of Public HealthUniversity of Pittsburgh
Essay Reader:Jeremy Martinson, DPhil ______________________________________Assistant ProfessorDepartment Infectious Diseases and MicrobiologyGraduate School of Public HealthUniversity of Pittsburgh
iii
Copyright © by Alec J. Howard
2016
ABSTRACT
Substance use disorders comprise a significant public health burden. In a 2015 survey,
the Substance Abuse and Mental Health Services Administration reported that 20.8 million
people aged 12 or older had a substance use disorder in the past year. Healthcare expenditure for
treatment in the U.S. was reported to cost $24.3 billon in 2009. Research has shown that genetic
factors may contribute to the development and maintenance of these disorders. The additive
effects of single-nucleotide polymorphisms have been implicated. Epigenetic mechanisms
involving microRNA, histone acetylation, and DNA methylation have been evidenced in
association with chronic substance use. Further research is necessary to determine the details of
underlying mechanisms of susceptibility to addiction. Utilizing evidence-based policy can have a
substantial impact on public health outcomes. Concern over rising levels of opioid-related
overdose death has warranted changes in health policy. This essay highlights health policy at the
federal, state, and local level regarding the opioid epidemic.
iv
Candace Kammerer, PhD
GENETIC FACTORS INVOLVED IN SUBSTANCE USE DISORDERS WITH REVIEW OF THE RISING OPIOID EPIDEMIC
Alec J. Howard, MPH
University of Pittsburgh, 2016
TABLE OF CONTENTS
PREFACE.................................................................................................................................VIII
1.0 ADDICTION.........................................................................................................................1
1.1 PUBLIC HEALTH BURDEN OF SUBSTANCE USE DISORDERS....................1
1.2 MECHANISM OF ADDICTION...............................................................................2
1.3 BEHAVIORAL ASPECTS OF ADDICTION...........................................................3
2.0 GENETIC FACTORS OF ADDICTION...........................................................................5
2.1 GENETICS INTRODUCTION AND IMPORTANCE IN PUBLIC HEALTH
GENETICS............................................................................................................................5
2.2 INTERACTION OF BEHAVIORAL GENETICS AND ENVIRONMENT.........6
2.3 SINGLE NUCLEOTIDE POLYMORPHISMS RELATED TO ADDICTION....7
2.3.1 Single SNPs as Risk Factors.............................................................................8
2.4 EPIGENETIC FACTORS RELATED TO ADDICTION.....................................10
2.4.1 MicroRNA.......................................................................................................11
2.4.2 DNA Methylation............................................................................................12
2.4.3 Histone Modification......................................................................................14
3.0 EXAMINING THE OPIOD EPIDEMIC.........................................................................16
3.1 OPIATES....................................................................................................................16
3.2 TREATMENT............................................................................................................16
v
3.3 RISING PROBLEM..................................................................................................17
3.4 CONTRIBUTING FACTORS..................................................................................18
3.5 POLICY REGARDING THE OPIOID EPIDEMIC..............................................19
3.5.1 Comprehensive Addiction and Recovery Act Of 2016................................19
3.5.2 Prescription Drug Monitoring Programs.....................................................20
3.5.3 Naloxone Overdose Prevention Law.............................................................22
3.5.4 Good Samaritan Overdose Law....................................................................23
3.5.5 Allegheny County Naloxone Standing Order...............................................23
4.0 DISCUSSION......................................................................................................................25
APPENDIX: TABLES.................................................................................................................29
BIBLIOGRAPHY........................................................................................................................30
vi
LIST OF TABLES
Table 1. State Overdose Prevention Legislation Summary...........................................................29
Table 2. Pennsylvania Compared to Selected States.....................................................................29
vii
PREFACE
I would like to thank Dr. Kammerer and Dr. Martinson for their valuable insight that contributed
to this work. I would like to thank Dr. Kammerer for her guidance, dedication, and support
throughout my study at the Graduate School of Public Health. I would also like to thank the
Department of Human Genetics for its commitment to the students and advancement of the field
of Public Health.
viii
1.0 ADDICTION
1.1 PUBLIC HEALTH BURDEN OF SUBSTANCE USE DISORDERS
The American Psychiatric Association defines addiction as, “a complex condition, a
chronic brain disease that is manifested by compulsive substance use despite harmful
consequence,” (APA). Symptoms of substance use disorders have four categories. They include
drug effects, risky use, social problems, and impaired control. The Substance Abuse and Mental
Health Services Administration (SAMHSA) is a federal agency that conducts the National
Survey on Drug Use and Health (NSDUH). In the 2015 survey, SAMHSA reported that 20.8
million people aged 12 or older had a substance use disorder in the past year. Of those 20.8
million, 15.7 million had an alcohol use disorder and 7.7 million had an illicit drug use disorder.
Overall, healthcare expenditure for substance abuse disorders was reported to cost $24.3 billon
2009 and expected to grow to $42.1 billion by 2020 (SAMHSA). Prevalence rates of some
addictive substances such as alcohol and cocaine have been declining in the past decade, while
heroin use has been increasing. According to the NSDUH, alcohol use disorders in individuals
18-25 years of age (n=3.8 million) have significantly decreased from 17.7 percent in 2002 to
10.9 percent in 2015 (p<0.05; SAMHSA). The rate of individuals aged ≥26 years (n=11.3
million) have significantly decreased from 6.2 percent in 2002 to 5.4 percent in 2015 (p<0.05;
SAMHSA). Similarly results in cocaine use disorders show individuals 18-25 years of age
1
(n=229,000) have significantly decreased from 1.2 percent in 2002 to 0.7 percent in 2015
(p<0.05; SAMHSA). The rate of individuals aged ≥26 years (n=637,000) have significantly
decreased from 0.6 percent in 2002 to 0.3 percent in 2015 (p<0.05; SAMHSA). By contrast,
results in heroin use disorders show individuals 18-25 years of age (n=155,000) have
significantly increased from 0.2 percent in 2002 to 0.7 percent in 2015 (p<0.05; SAMHSA). The
rate of individuals aged ≥26 years (n=430,000) was stable at 0.1 percent from 2002 until 2010
(p<0.05; SAMHSA). Then, the rate significantly increased from 0.1 percent in 2011 to 0.2
percent in 2015 (p<0.05; SAMHSA). Consequently, these disorders represent a significant public
health burden.
1.2 MECHANISM OF ADDICTION
The mechanisms of addiction have been examined at the molecular level in human and
rodent models. Opium and its derivatives act as analgesics and bind to receptors that are
normally used by endorphins and enkephalins (sometimes referred to as endogenous opiates)
(Sahbaie et al. 2016). Prolonged drug abuse in opioid users results in remodeling of synapses in
the central nervous system, referred to as a disorder of experience-dependent neuroplasticity
(Russo et al., 2009; Kasenetz et al., 2010). In cocaine addiction, use of the drug blocks the
transporter protein channels that would normally return the neurotransmitter, dopamine, to the
axon. Instead, the neuron becomes over-stimulated and the result is production of fewer
receptors in the postsynaptic membrane (Schmidt et al., 2013). If the user abstains from cocaine
use, then normal reuptake of dopamine would continue. Since there are fewer receptors
available, the sensitivity of the nerve pathway lessens. Users now need cocaine to maintain
2
normal levels of activity since it intensified their pleasurable sensations. Over time there can be
an increased need for a higher and higher dose of the substance to achieve the same level of
sensation, known as tolerance (Russo et al., 2009; Liang et al., 2013). Overall, heavy use of these
drugs cause long-term damage to the pleasure and reward circuitry of the brain (Kasenetz et al.
2010).
1.3 BEHAVIORAL ASPECTS OF ADDICTION
Characteristics of drug addiction involve a number of behaviors including but not limited
to uncontrolled drug consumption, deficits in behavioral inhibitory control, and high rates of
relapse in periods of attempted abstinence. Effective inhibitory control is used in everyday life in
order to withhold socially inappropriate thoughts such as peculiar or spiteful expressions. Smith
et al. (2014) published a meta-analysis of 97 studies that compared addiction-like behaviors or
heavy substance abuse users (determined using DSM-IV criteria, n=3405) with healthy controls
(no history of substance use, n=3119)(Smith et al., 2014). These studies ranged in participant
size from 38 to 620, ranged from zero to seventy-five percent female and included an average
age of 50 years (Smith et al., 2014). Using common measures of inhibitory control, Go/NoGo
task and Stop-Signal Task, they discovered that inhibitory deficits of significant small to medium
effect were present for individuals who exhibited heavy use/dependence on alcohol (g=0.345,
p<0.004), tobacco (g=0.248, p<0.037), cocaine (g=0.464, p<0.001), MDMA (g=0.351, p=0.047)
and methamphetamine (g=0.724, p<0.001) (Smith et al., 2014). However, they reported no
differences in inhibitory deficits between controls and individuals who reported heavy
use/dependence on opioids (N=270) or cannabis (N=739) (Smith et al., 2014). This review did
3
not report the geographic location or ethnicity; however, the evidence suggests that some classes
of drugs may involve and influence different behaviors.
Opioids have been recently associated with specific personality characteristics as risk
factors for susceptibility to opioid abuse (Zaaijer et al., 2014). The study assessed possible
differences in a variety of personality traits between individuals who had taken opioids, but were
never-dependent (n=161), and individuals who were dependent and were receiving methadone
maintenance or heroin-assisted treatment (n=402) in the Netherlands. Personality traits were
assessed using a truncated version of Cloninger’s Temperament and Character Inventory. This
population comprised of Caucasian individuals from the Netherlands who were at least 25 years
of age with a mean age of 40. Never-dependent opioid users exhibited a tendency to seek novel
and/or spiritual experiences, as well as harm avoidance (d=0.49, p<0.0024) (Zaaijer et al., 2014).
Interestingly, the never-dependent individuals exhibited higher levels of reward dependence than
dependent individuals (d=0.42, p<0.0024); therefore, they may have been protected against
opioid dependence as a result. Furthermore as compared to controls, never-dependent opioid
users reported higher self-directedness, a measure of self-efficacy, (d=0.55, p<0.0024) (Zaaijer et
al., 2014), which may have helped them better adapt and make choices regarding their actions. In
addition to reporting lower self-directedness, dependent users reported significantly higher harm
avoidance than never-dependent users (d=0.58, p<0.0024) (Zaaijer et al., 2014). These findings
are suggested to become important for planning and prevention strategies as part of adolescent
assessments for those believed to be at higher risk of opioid dependence. By using this strategy,
it could become possible to identify those at risk and target them with prevention efforts to avoid
behaviors that could lead to opioid dependence.
4
2.0 GENETIC FACTORS OF ADDICTION
2.1 GENETICS INTRODUCTION AND IMPORTANCE IN PUBLIC HEALTH
GENETICS
Understanding genetic mechanisms underlies research in all fields of the life sciences.
Genes are composed of DNA, deoxyribonucleic acid, which has been referred to as the building
blocks of life. Specifically, DNA is composed of two strands of four different molecules called
nucleotides. They are cytosine, guanine, thymine, and adenine (C, G, T, A). The central dogma
in genetics is that genes (strands of DNA) are transcribed into a copy composed of single-
stranded RNA (ribonucleic acid), which is then translated into a protein. Proteins serve roles
throughout and between cells as part of life’s functions. Mutations in DNA can lead to
significant differences in our appearance and health. These differences appear as a phenotype,
defined as the “observable physical and/or biochemical characteristics of the expression of a
gene” (NCBI). Study of these factors provides the foundational research, which can then be used
for treatment strategies of addiction in some cases.
Like many fields of science, the study of human genetics is an ever-expanding and
continually advancing field. Genetics is defined as the study of genes, their expression and their
heredity in organisms. By utilizing this field, it has become possible to understand how traits
vary between individuals, how traits are passed to offspring, and influence our mental and
5
physical characteristics. Human genetics has the potential to make a significant difference in
public health, specifically by providing foundational research and aiding development of clinical
treatment. Genetics has become prevalent in many specialties of science. One such specialty is
psychiatrics where research has examined various content areas relating to biologic, social and
cognitive functioning (McAdams et al., 2013; Zaaijer et al., 2014). Susceptibility to substance
abuse disorders is believed to aggregate in families. Relatives of individuals with suffering from
addiction are considered be at, “4-8 fold increase in risk of substance abuse disorders in
individuals with an affected first-degree relative,” according to a review by researcher Paul
Kenny, PhD (2014). Due to the rising prevalence and public health burden of addiction,
understanding the potential mechanisms of psychiatric disorders, such as substance abuse
disorders, is beneficial and important to public health. Much of the research into substance abuse
disorders began focus in families in which single gene variants (SNPs) were examined and more
recently epigenetic mechanisms (discussed later). This would indicate that these disorders could
be attributed to multiple factors including those in the environment.
2.2 INTERACTION OF BEHAVIORAL GENETICS AND ENVIRONMENT
Environmental measures such as parenting and life events may influence (possibly
genetically determined) behavioral phenotypes in offspring (McAdams et al., 2013). Using self-
report questionnaires, researchers examined a sample of adolescent participants (n=2647)
comprising sixty-nine percent female with a mean age of 14.78 (range 13-17, SD=1.36), all
monozygotic or dizygotic twin pairs (n=328 and n=774, respectively)(McAdams et al., 2013).
Since monozygotic twins share 100 percent of their genetic make-up and dizygotic twins share
6
50 percent, researchers focused on the role of those shared genetic traits (behavioral phenotypes)
in correlation with their experiences of life events and parental negativity. The result showed that
negative life events (such as “suspension from school” or “being sent away from home”) showed
moderate to strong genetic correlation with depression (0.57[CI: 0.26-0.97], p<0.01),
oppositionality (0.95[CI: 0.62-0.99], p<0.01), delinquency (0.99[CI: 0.73-1.00], p<0.01), and
physical aggression (0.68[CI: 0.36-0.96], p<0.01) (McAdams et al., 2013). It was hypothesized
that humans select, modify, and create environments that are correlated with their genetically
determined personality and psychopathology. These correlations are some of the most replicated
findings in behavioral genetics, according to a recent review by Plomin et al. (2016). This would
support the idea that genes and environment have an interaction that results in observable
phenotypes.
2.3 SINGLE NUCLEOTIDE POLYMORPHISMS RELATED TO ADDICTION
A SNP is a mutation that is a single nucleotide base change in the DNA sequence. SNPs,
also called single genetic variants, have been shown to have associations with substance use
disorders. Common SNP’s are those that are found occurring frequently (0.05 allele frequency or
higher) compared to rare variants in a population. A single common SNP is generally believed to
have small effect on phenotypes (Manolio et al., 2009). However, multiple common SNPs added
together could potentially have a larger effect (Manolio et al., 2009). In a recent study by Palmer
et al. (2015), the effects of common SNPs were examined in a predominantly European ancestry
population (n=2596) taken from multiple data sets in the U.S. in which subjects had reported
addiction to or problem use of alcohol, tobacco, cannabis, or illicit drugs (Palmer et al., 2015).
7
Their evidence supports that heritability of common SNPs is associated with generalized
vulnerability to substance use disorder traits including diagnosed dependence (h2SNP=0.36,
SE=0.13, p=2.30x10-3), problem use of substances (h2SNP=0.36, SE=0.13, p=2.30x10-3), and
dependence vulnerability (h2SNP=0.36, SE=0.13, p=2.30x10-3) compared to an independent
case-controls (Palmer et al. 2015). They attributed at least 20 percent of the general vulnerability
to substance dependence to heritability of common SNPs and hypothesized that common SNPs
could be used to index the genetic liability of comorbid drug problems. However, the total
contribution of all of the variants to the phenotype was modest (25-36%); again indicating that
environmental factors have strong effects on susceptibility (Palmer et al., 2015). Similar results
have been replicated in a genome-wide association study of alcohol dependence in an African
American population (n=2875). Researchers Yang et al. (2014) revealed evidence that their array
of 769,498 common SNPs accounted for 23.9 percent (SE=9.3%, p<0.05) of the variance.
2.3.1 Single SNPs as Risk Factors
Single SNPs can be risk factors for addiction. Strong associations of single SNPs have
been found in association with addiction. In a recent candidate gene study of 122 variants in 26
stress-related genes, Levran et al. (2014) uncovered evidence supporting that stress-related genes
may contribute to heroin addiction in an African American sample comprising 314 cases (37%
female) and 208 controls (52% female)(Levran et al., 2014). Heroin addiction was significantly
associated with variants rs1360780 of intron 2 (OR=2.35, pcorrected=0.03) and rs3800373
(OR=2.85, pcorrected=0.0018) of the 3`UTR in the FKBP5 (FK506 binding protein 5) gene,
possibly by modulating the stress response (Levran et al., 2014). One of the functions of
corticotrophin-releasing hormone (part of the body’s stress response system) is to stimulate the
8
mesocorticolimbic dopamine system, which mediates the rewarding effects associated with drug
use. Therefore, these variants of the FKBP5 gene, the product of which functions to co-
chaperone regulation of glucocorticoid sensitivity, possibly combined with other stress-related
gene variants, could contribute to heroin dependence. Genes in other biochemical pathways have
been associated with opioid dependence as well. A recent genome-wide association study
(Nelson et al., 2016) reported an association between susceptibility to opioid abuse and variants
in the CNIH3 gene in an Australian population (n=1328) comprising individuals 18 years and
older that were either opioid-dependent daily injectors or non-dependent users. The CNIH3
(cornichon family AMPA receptor auxiliary protein 3) gene encodes a protein subunit that is part
of a receptor in the AMPA glutamate system. Its expression is highest in the frontal cortex,
amygdala, and hippocampus; the amygdala is normally associated with habituation. Specifically,
results of the study revealed that CNIH3 SNP rs1436175 (OR=0.50[0.39-0.64], p=2.72x10-8) was
associated against progression to opioid dependence in opioid users. The AMPA glutamate
system is a primary excitatory neurotransmitter system in the central nervous system. Thus, this
association is consistent with previous research on the involvement of addiction disorders in
synaptic plasticity (Kasenetz et al., 2010). Overall, the above studies indicate that multiple
common SNPs of small but additive effect and single SNPs may mediate genetic risk for
substance use addiction. However, SNPs may also be protective against substance abuse
disorders.
9
2.4 EPIGENETIC FACTORS RELATED TO ADDICTION
Much of the research into genetic influence on substance abuse disorders includes focus
on epigenetic mechanisms such as microRNAs, DNA methylation, or histone modification.
Epigenetic refers to epi- meaning “on top of” or “in addition to” and genetics, the study of genes,
their heritability in organisms. As opposed to modifications at the nucleotide level of DNA such
as a single nucleotide polymorphism, modifications at the chromosomal level occur throughout
development; the study of which is known as epigenetics. Epigenetic effects are believed to
modify how genes are expressed, either increasing or decreasing expression. In some instances,
these changes can increase expression, decrease expression, or silence expression all together.
One gene in particular has been associated repeatedly with drug addiction, possibly so by
epigenetic mechanisms. Epigenetic regulation of the brain-derived neurotrophic factor (BDNF)
gene, which is expressed throughout the nervous system, has been the subject of overlapping
research in human, rodent and frog studies. The BDNF protein is involved in higher cognitive
functions in addition to critical regulation of the structure and function of neuronal circuits (Im et
al., 2010). In a recent review of 79 studies, McCarthy et al. (2012) drew attention to the effect by
which addictive substances are believed to induce changes in BDNF expression in the
mesolimbic dopamine pathway. For example, chronic morphine exposure in a mouse model (n=5
per group) increased BDNF expression 1.5 fold (p<0.01) compared to controls in spinal cord
tissue, resulting in persisting neuroadaptations (Liang et al., 2013). Neuroadaptations are changes
in the responsiveness of a neuron or neurons in a system to a stimulus, as is the case in chronic
exposure to addictive substances; this is the physiological change involved in tolerance. Further
research is warranted to elucidate the exact mechanisms.
10
2.4.1 MicroRNA
MicroRNAs (miRNA) are small non-coding strands of RNA (~19-23 nucleotides in
length) that up-regulate or down-regulate gene expression by binding to the 3’ untranslated
region (UTR) of their targets, mRNA transcripts. Research has implicated various miRNAs in
modulation of cocaine reward and withdrawal (Brown et al., 2013). Expression of one in
particular, miR-212, showed a significant reduction (t10=2.876, p=0.01) in the dorsomedial
striatum region of the brain of rats phenotyped as addiction-vulnerable (n=6) compared to
addiction resilient (n=6)(Quinn et al., 2015). Interestingly, these findings were made up to 8
weeks after drug exposure, which supports the persistent neuroadaptations and propensity of
relapse in addicts caused by chronic self-administration of cocaine (Kasenetz et al., 2011). This
finding is important since down-regulation of miR-212 in the MeCP2 gene is hypothesized to
protect against detrimental cell signaling pathways that result in drug seeking (Im et al., 2010).
Other studies of addictive substances have shown similar results. A self-administering
methamphetamine rat addiction study (n=22) has shown association with upregulated expression
of miR-181a-2 (fold (log2)= -0.69, padj=0.00085) compared to controls as a result within the
ventral tegmental area of the brain (Bosch et al., 2015). Even though this study administered
short access to methamphetamine (20 daily injections followed by 14 days abstinent) as opposed
to chronic exposure (such as daily use for one year), these results show an important role of
miRNAs having influence in vulnerability to addiction (Bosch et al., 2015). Association between
alcohol use disorder and miRNA has been identified (Lewohl et al., 2011). Using human brain
tissue from former alcoholics (n=14, WHO criteria: average 80kg per day alcohol consumption)
compared to sex-matched controls (n=13, WHO criteria: <20kg per day alcohol consumption),
expression of 48 miRNAs was significantly increased (mean 27±11%, p<0.005) with miR-1
11
having been increased the most (45%, padj=0.044) in association with alcohol dependence
(Lewohl et al., 2011). Another study of short term (1 hour) alcohol exposure has shown the
modularity of miRNA expression in relation to the big potassium (BK) channel, a protein that
plays a dominant role in shaping neuronal activity and is strongly influenced by alcohol
consumption (Pietrzylowski et al., 2008). This in vitro study, involving the regulation of miR-9
expression, revealed an increase of miR-9 expression in neurons of the supra-optic nucleus
(50%, p<0.05) and the striatum (2 fold, p<0.05) of rats (n=27) compared to saline controls while
BK transcripts decreased 80 percent (p=0.01)(Pietrzylowski et al., 2008). This rapid response to
alcohol is important to note, since the BK channel has very high conductance and its dysfunction
could result in serious consequences for the central nervous system. Although many studies
report associations between miRNA expression and alcohol use disorders, the exact mechanisms
underlying these associations are unclear. However, increased mi-RNA expression is considered
to result in decreased protein expression, which can lead to neural deterioration (Most et al.,
2014). Overall, miRNAs modulate gene expression in cases of chronic use of addictive
substances.
2.4.2 DNA Methylation
Another type of epigenetic mechanism is DNA methylation. DNA methylation is the
process of methyl groups being added to DNA, cytosine and guanine dinucleotides. These small
changes may repress gene expression and thus, significantly impact a trait. Knothe et al. (2016)
examined the potential role of DNA methylation regulating expression of the mu-opioid receptor
gene, OPRM1. Using human cell lines, mRNA expression and methylation levels were examined
in the non-neuronal mu-opioid receptor non-expressing HEK-293 cell line, the mu-opioid
12
receptor expressing neuronal-derived SHSY5Y cell line, and the neuronal mu-opioid receptor
non-expressing Kelly cell line. Over their 22 selected CpG positions, findings included OPRM1
methylation in Kelly cells to be higher on average (80% C-methylation, F=216.7, p<0.001)
compared to SHSY5Y cells (3% C-methylation, F=216.7, p<0.001) (Knothe et al., 2016). This
corresponded to no OPRM1 mRNA expression in Kelly cells (none compared to β-actin,
F=63.72, p<0.0001) compared to OPRM1 mRNA expression levels in SHSY5Y cells (5.9x10-6
compared to β-actin, F=63.72, p<0.0001) (Knothe et al., 2016). These differences suggest that
epigenetic regulation of the mu-opioid receptor through changes in DNA methylation does
occur. They also highlighted the possibility that effects of other epigenetic mechanisms could
mask the effects of DNA methylation. In a mouse model of cocaine administration and
protracted abstinence (N=53), researchers Baker-Andresen et al. (2015) found persistent
methylation changes in neuronal DNA isolated from the medial prefrontal cortex. Following 10-
12 days of cocaine self-administration, mice were abstinent for 21 days and given a relapse test
to expressly reactive cocaine memories. Findings revealed a demethylation (-37.5% relative 5mC
enrichment) of the Golgb1 gene compared to naïve mice (F3,17=3.57, p<0.05) (Baker-Andresen et
al., 2015). This decrease was correlated with a decrease in Golgb1 gene expression (-20%
relative expression) compared to naïve mice (F3,26=5.42, p<0.01) (Baker-Andresen et al., 2015).
This evidence suggests that altered DNA methylation could cause long-term abstinence
associated behavioral adaptations such as maintenance of cocaine-related memories and
continued cocaine seeking. Overall, research supports that DNA methylation is involved in
epigenetic gene expression and associated aspects of addiction including self-administration and
abstinence.
13
2.4.3 Histone Modification
Another type of epigenetic mechanism is histone modification. Histones are proteins that
package the DNA so that it can be stored into compact structural units, and they play a role in
gene regulation. Histone modifications include acetylation, phosphorylation and ubiquitination.
Histones can become acetylated or de-acetylated via specific enzymes such as HAT (histone
acetyltransferase) or HDAC (histone deacetylase), which is important since this is the manner in
which genes can be expressed or not. Increased acetylation is considered to increase expression
while decreased acetylation decreases expression (Schmidt et al., 2013). Increased acetylation
(3.5 fold enrichment, p<0.001) of the histone H3K9 was shown to be associated with morphine
in up-regulating expression of the BDNF (3 fold, p<0.001) and PDYN (5 fold, p<0.001) genes,
which are implicated in neuronal pathways (Sahbaie et al., 2016). This discovery was made
while investigating enhanced postoperative pain and analgesic tolerance after continuous opioid
exposure in their mouse model (n=5 per group). Other examples of histone acetylation have been
implicated in a heroin self-administration rat model (n=21; Xu et al., 2016). Compared to passive
yoked controls, an association between heroin self-administration and deacetylation of histone
H4K5 (2.44 fold, p=0.013) in relation to BRG1 gene expression was found in a specific brain
region of the mesocorticolimbic dopamine system known as the ventral tegmental area (VTA)
(Xu et al., 2016). Their results implicated heroin administration, not specifically addiction in this
study, but the association is another example of histone acetylation exerting an influence relating
to gene expression. Other areas of the central nervous system show changes in gene expression
resulting from histone acetylation (Liang et al., 2013). In another mouse model (n=5 per group)
of opioid-induced hyperalgesia (abnormally heightened sensitivity to pain), dependence and
tolerance, resulting evidence supported acetylation of histone H3 in the dorsal horn of the spinal
14
cord being increased (2 fold, p<0.05) after chronic opioid exposure (Liang et al., 2013). This
would suggest that these associations between histone acetylation and opioid dependence occur
through the central nervous system. Overall, histone modifications, specifically acetylation, are
implicated in epigenetic gene expression and associated addiction, though the exact mechanisms
require further research.
As described above, the additive effects of multiple genetic variants, as well as,
epigenetic mechanisms involving miRNA, histone modification, and DNA methylation have
been associated with substance use disorders in multiple studies involving human and rodent
models. Further research is warranted to determine the details of underlying mechanisms of
susceptibility to addiction.
15
3.0 EXAMINING THE OPIOD EPIDEMIC
3.1 OPIATES
The poppy plant is the natural source of opium and has been commonly used for
hundreds of years. Opium and its derivatives including morphine and codeine have been used for
medicinal purposes and are considered to be unsurpassed in terms of pain management.
Additionally, opioids are prescribed for a variety of other symptoms such as cough and diarrhea.
Their effects include feelings of sedation, tranquility and euphoria. As mentioned previously,
they are highly addictive and exhibit tolerance in humans as well as animal models. In higher
doses, they lead to such levels of sedation that will overwhelm respiratory functions and result in
death (Schuckit, 2016).
3.2 TREATMENT
Physical dependence can be treated using administration of methadone and
buprenorphine by specialty programs. These treatments must be administered by physicians and
other healthcare professionals with specialty training and comprise a gradual reduction of doses
over the course of weeks in a controlled environment. An alternative method to facilitate
withdrawal utilizes a combination of α2-adrenergic antagonists, benzodiazepines, loperamide,
16
prochlorperazine, sports drinks or intravenous fluids, and non-steroidal anti-inflammatory agents.
In addition, emotional and psychological support through self-help programs and education help
to rehabilitate former users (Schuckit, 2016).
3.3 RISING PROBLEM
In recent years, rising levels of opiate overdose related death has garnered the attention of
public health agencies. The Substance Abuse and Mental Health Services Administration
(SAMHSA) is a federal agency that conducts the National Survey on Drug Use and Health
(NSDUH). In the 2015 survey, SAMHSA reported that 20.8 million people aged 12 or older had
a substance use disorder in the past year. Of those 20.8 million, 2 million people had a pain
reliever use disorder and 591,000 had a heroin use disorder. SAMHSA also reported that there
has been a statistically significant increase in heroin use disorders from 2010 compared to 2015.
Part of the NSDUH includes regional maps which indicate that annual averages from 2012-2014
showed that the region of Allegheny County reported that the percentage of persons who
reported illicit drug dependence or abuse in the past year was 3.16-3.46 percent; the majority of
the Commonwealth of Pennsylvania (PA) was 2.82 percent or lower (SAMHSA). In 2013, the
CDC reported that the rate of drug poisoning deaths in PA was 19.4, higher than the U.S. rate of
13.8 (CDC, 2013). Community-based partnerships have begun to emerge as a result.
OverdoseFreePA is a collaboration of six partner organizations and sixteen Pennsylvania
communities that seek to increase awareness and support initiatives to address the issue of
substance use disorders. Data on unintentional overdoses is collected from death records
submitted from the county coroner and medical examiner’s office. Their data reflects the
17
increasing prevalence of heroin related overdoses in Allegheny County. Between the years 2008-
2016, heroin is reported as the leading substance in overdose death; it comprises 1240 deaths out
of an overall 2310 (53.7%) total substance overdose deaths (OverdoseFreePA, 2016).
3.4 CONTRIBUTING FACTORS
The rise of what is now known as the opiate epidemic has been supported by a number of
factors. Journalist and author Sam Quinones (2015) investigated the problem in depth in his
work Dreamland: The True Tale of America’s Opiate Epidemic. Through his comprehensive
analysis of the issue, Quinones found that black tar heroin has been an illicit opiate that
contributed significantly to this issue. Its production and efficient distribution allowed its sale to
reach across the U.S. (Quinones, 2015).
Another main contributing factor to the opioid epidemics is over-prescription for medical
uses. Research from the CDC regarding this matter has been published in their Morbidity and
Mortality Weekly Report (MMWR). The CDC examined data from the IMS Health’s National
Prescription Audit. They discovered that variation exists among states in prescribing opioid pain
relievers. Out of the estimated 258.9 million prescriptions written in 2012, prescribers wrote 82.5
opioid pain reliever prescriptions per 100 persons in the United States and the rates between
states varied 2.7 fold. The three highest prescribing states were Alabama, Tennessee, and West
Virginia. The CDC concluded that the high rates indicated need to identify prescribing practices,
since practitioners may not be appropriately balancing patient safety and pain relief (CDC,
2014).
18
3.5 POLICY REGARDING THE OPIOID EPIDEMIC
3.5.1 Comprehensive Addiction and Recovery Act Of 2016
Health policy at the federal level has been implemented via the Comprehensive Addiction
and Recovery Act of 2016 (CARA, S. Bill. 524, 2016) that was recently passed in July 2016.
This law responds to the growing epidemic from various perspectives. Title I calls for
implementation of a task force of agencies including Health and Human Services (HHS),
Department of Justice (DOJ), Department of Veteran Affairs (VA), Drug Enforcement
Administration (DEA), and Centers for Disease Control and Prevention (CDC). Title I focuses
on prevention and education through expansion of community-based coalitions and awareness
campaigns. Title II focuses on law enforcement and treatment through development of treatment
alternate to incarceration programs, first responder training of naloxone use, prescription drug
take back expansion and specific heroin and methamphetamine task forces. Title III focuses on
treatment and recovery through expanding evidence-based prescription opioid and heroin
treatment interventions, criminal justice medication assisted treatment and interventions, national
youth recovery initiative, and building communities of recovery. Title IV addresses collateral
consequences through implementing correctional education demonstration programs and creating
a national task force on recovery and collateral consequences. Title V focuses on addiction and
recovery services for women, families, and veterans through improving treatment for pregnant
and postpartum women, collecting reports on grants for family-based substance abuse treatment
and veterans treatment court changes. Title VI focuses on incentivizing state comprehensive
initiatives to address prescription opioid and heroin abuse through implementing state
demonstration grants to prepare comprehensive state or local government plans to combat the
19
issue. Title VII contains miscellaneous sections including programs to prevent prescription drug
abuse under Medicare as well as reports regarding the impact of Medicaid Institutions for Mental
Disease exclusion has on access to treatment. Title VIII focuses on transnational drug trafficking
by outlawing possession, manufacture or distribution for purposes of unlawful importations (S.
Bill. 524, 2016). CARA is comprehensive in its approach and outlines expansion of existing
policy at the state and local level. Major health policy examples include naloxone distribution
programs, prescription drug monitoring programs, and Good Samaritan laws.
3.5.2 Prescription Drug Monitoring Programs
One potential policy response to address this problem is use of Prescription Drug
Monitoring Programs (PDMP), which are state-based databases of prescriber, dispenser and
patient information. As of May 2016, 49 states and the District of Columbia have operational
programs; Missouri is the only state that does not (NAMSDL). In a separate report, the CDC
analyzed data from the Florida Medical Examiners Commission and prescription drug
monitoring policy changes. In 2010, Florida’s state legislature made policy changes by
implementing laws that regulated pain clinics. In 2011, new policy prohibited prescribers from
dispensing opioid analgesics from their offices, and dispensers were required to report data to the
newly established Prescription Drug Monitoring Program. Between 2010-2012, their overdose
death rate for opioid analgesics declined from 13.6 to 9.9 per 100,000 persons, a 27.0% decrease,
and the overall drug overdose death rate declined from 17.0 to 14.0 per 100,000 persons, a
17.7% decline (CDC, 2014). Other states have seen similar results. The Prescription Drug
Monitoring Program Center of Excellence (PDMPCOE) at Brandeis University is a project
funded by the U.S. Department of Justice, Bureau of Justice Assistance. The Center of
20
Excellence released a briefing in 2016 showing similar results in Kentucky, Tennessee, New
York and Ohio. In each state example, PDMP enrollment increased while prescriptions
dispensed decreased, following legislation that mandated use of PDMPs. In 2012, Kentucky
passed legislation requiring prescribers to enroll and utilize its PMDP. Enrollment increased
from 7,911 users in April 2012 to 25,409 users in July 2013, a three-fold increase. Dispensed
controlled substances declined from 7.39 million doses in a one-year period (August 2011-July
2012) to 6.76 million doses in the next year period (August 2012-July 2013), an 8.5 percent
decrease. In January 2013, Tennessee passed legislation requiring prescribers to enroll in and
utilize its PMDP. Enrollment increased from 15,232 providers in 2011 to 42,835 providers in
2015. The number of opioid prescriptions reported declined from 8,778,561 in 2012 to 8,084,981
in 2015, an 8 percent decrease. In June 2013, New York passed legislation requiring prescribers
to enroll in and utilize its PMDP. Enrollment increased from 5,087 users in August 2013 to
67,779 users in February 2014, a 6 month time period. The number of all opioid prescriptions
reported declined 9.5 percent. In October 2011, Ohio passed legislation requiring licensing
boards to create regulations for prescribers to enroll in and utilize its PMDP. The number of
reports queried increased from 911,000 in 2010 to 7.3 million in 2013. The number of
hydrocodone doses dispensed declined 3.5 percent and the number of prescriptions declined 11.1
percent. The number of oxycodone doses dispensed declined 1.7 percent and the number of
prescriptions declined 8.7 percent (PDMPCOE, 2016).
Prescription Drug Monitoring Programs (PDMP) have been in effect for a number of
years. Recent changes in health policy have called for their expansion (CARA). Pennsylvania’s
Prescription Drug Monitoring Program collects information on all filed prescriptions for
controlled substances in an effort to allow healthcare providers to safely prescribe controlled
21
substances such as opioid pain medication. The General Assembly of the Commonwealth of
Pennsylvania signed into law Act 191 (S. Bill. 1180, 2014) in 2014, attempting to achieve better
care by monitoring. Prescribers are required to query the system for each patient the first time the
patient is prescribed a controlled substance or if the prescriber has reason to believe that a patient
is abusing a controlled substance. The prescriber may then enter information into a patient’s
medical record if the patient shall not be prescribed or furnished a controlled substance based on
information found in the system. Dispensers are required to provide the full name of the
prescriber, their DEA registration number, the date the prescription was written, the date it was
prescription was dispensed, the quantity and days’ supply, the DEA registration number and
National Provider Identifier of the dispenser, and method of payment. Furthermore, the full
name, date of birth, gender and address of the person for whom the prescription was written and
dispensed is also required (S. Bill. 1180, 2014).
3.5.3 Naloxone Overdose Prevention Law
Naloxone, or Narcan, is a life-saving drug that is effective in stopping and reversing the
effects of an opioid overdose (Wermling, 2015). Naloxone has become a mainstay of emergency
response as evidenced by its inclusion in federal policy, CARA, and PA health policy. There are
a total of 46 jurisdictions that have naloxone overdose prevention laws, including the
aforementioned PA and also the District of Columbia. Senate Bill 1164 of the General Assembly
of the Commonwealth of Pennsylvania signed into law Act 139 (P.L. 2487) in 2014 regarding
Naloxone distribution and drug overdose response immunity. The act authorizes law
enforcement officers and firefighters who have completed proper training to administer
Naloxone to an individual believed to be undergoing an opioid-related drug overdose. Act 139 of
22
2014 has authorized a Standing Order DOH-002-2016, which permits healthcare professionals
otherwise authorized to prescribe Naloxone to prescribe it via standing order. It permits
individuals to obtain Naloxone from a pharmacy in the event that they cannot obtain it from their
regular healthcare provider or another source. The Act provides immunity from prosecution for
any person who calls for emergency services and stays with the affected individual until they
arrive, and it prohibits prosecution against parole violations. Furthermore, the Act prohibits
prosecution of persons experiencing drug overdose events. This part of the law would be referred
to as Good Samaritan overdose prevention (S. Bill. 1164, 2014).
3.5.4 Good Samaritan Overdose Law
Good Samaritan overdose laws were created to provide some form of immunity from
prosecution for individuals who call emergency responders in cases of illicit drug overdoses. The
manner in which these laws are written varies by state. As of July 2016, there are 36 jurisdictions
that feature some form of these laws (see Tables 1 and 2). Pennsylvania is one of them, joined by
Kentucky, Florida, New York, and Tennessee. It is noteworthy that some of these states are also
the ones that have shown success in PDMP implementation. By contradiction, Ohio does not
have Good Samaritan laws in effect, as of July 2016 (see Table 2)(Legal Science, 2016).
3.5.5 Allegheny County Naloxone Standing Order
At the local level, health policy in Allegheny County has been implemented in response
to this rising problem. As of 2015, Dr. Karen Hacker, the director of the Allegheny County
Health Department (ACHD), issued a countywide standing order, citing PA Act 139, to allow
23
pharmacists throughout the county to distribute Naloxone without a prescription. The only other
of the 67 counties in PA that has a similar standing order is Montgomery County (ACHD, 2015).
24
4.0 DISCUSSION
Substance use disorders comprise a significant public health burden in the U.S. In the
National Survey on Drug Use and Health of 2015, the Substance Abuse and Mental Health
Services Administration reported that 20.8 million Americans had a substance use disorder,
which cost $24.9 billion in healthcare expenditure. Recent studies have revealed that many
factors including genetic, behavioral and environmental contribute to the development and
maintenance of addiction. Inhibitory control deficits are present in individuals who exhibit heavy
use or dependence on alcohol, tobacco, cocaine, MDMA, and methamphetamine, but not on
opioids or cannabis (Smith et al., 2014). Substance use disorders are believed to aggregate in
families, suggesting a genetic component. First-degree relatives of affected individuals are
believed to be at 4-8 fold increased risk of developing of substance use disorders (Kenny, 2014).
Environmental measures such as parenting or life events may influence genetically determined
behavioral phenotypes (McAdams et al., 2013). The BDNF gene has been the subject of
overlapping research in which its epigenetic regulation is associated with drug addiction
(McCarthy et al., 2012). The additive effects of multiple common SNPs have been evidenced
showing modest heritability of susceptibility to substance use disorders (25-36%; Palmer et al.,
2015). Additionally, single SNPs in the FKBP5 gene have shown to be risk factors for addiction,
while a SNP in the CNIH3 is associated with protection against opioid dependence (Levran et al.,
2014; Nelson et al., 2016). Epigenetic mechanisms involving expression miRNAs miR-9, miR-
25
181, miR-1, and miR-212 have shown association with addiction (Pietrzylowski et al., 2008;
Lewohl et al., 2011; Bosch et al., 2015; Quinn et al., 2015). Histone modification including
acetylation of histone protein H3K9 and deacetylation of histone protein H4K5 has been
associated with opioid addiction (Liang et al, 2013; Sahbaie et al., 2016; Xu et al., 2016).
Furthermore, increased DNA methylation of the OPRM1 gene and demethylation of the Golgb1
gene has been associated with substance use disorders as well (Baker-Andresen et al., 2015;
Knothe et al., 2016). However, all of these genetic factors do not fully explain the addiction
phenotype, which means the combination of genetic and environmental factors play a role. It is
difficult to say just how much is attributed to each, but studies support that 20 to 30 percent of
the susceptibility could be due to genetic factors while the other 70 to 80 percent could be
attributed to environmental. Further research is necessary to determine the details of the exact
mechanisms that underlie susceptibility to addiction.
Utilizing evidence-based policy can have an impact on public health outcomes, such as
the successful cases of Prescription Drug Monitoring Programs in Florida, Tennessee, New
York, Kentucky and Ohio. Concern over rising levels of opioid-related overdose death has
warranted changes in health policy. This essay has highlighted health policy at the federal, state,
and local level regarding the opioid epidemic. The Comprehensive Addiction and Recovery Act
of 2016 is an example of recent federal level policy recently passed to address the issue of the
rising opioid epidemic. Health policy at the state level has also responded, such as
Pennsylvania’s Act 139 (S. Bill. 1164, 2014). Interestingly, 46 jurisdictions feature Naloxone
overdose prevention laws but only 36 of them feature Good Samaritan provisions that allow
protective immunity for those involved in an opiate overdose. New Mexico was the first state to
pass this type of law in 2007. By comparison, PA passed these laws in 2014. Even though their
26
intent is similar, that is to address the epidemic, not all of these Naloxone state laws are exactly
the same. If law enforcement responds to your overdose in Pennsylvania you will not be charged
for possession of an illicit opiate. If you have an overdose involving an illicit overdose in Ohio
though, you can be charged for possession of that substance. The Good Samaritan provisions
may foster change in the health outcomes of this rising problem. Regardless of good intention,
unintended consequences will arise as a result of these policies. In an effort to deter abuse of
prescription opioid OxyContin, a new formulation of the pharmaceutical was released in 2010. It
was made more insoluble so that it cannot be crushed and dissolved into saline solution for
injection like heroin (Dart et al., 2015). Interestingly, the National Poison Data System and
Poison Center Program released data showing a spike in the rate of heroin use from 0.12 per
100,000 population in 2010 to 0.23 per 100,000 population in 2013, while the rate of OxyContin
Abuse decreased from 0.07 per 100,000 population in 2010 to 0.02 per 100,000 population in
2010 (Dart et al., 2015). Nevertheless, evidenced-based policies provide a promising start, which
was the case in the Prescription Drug Monitoring Programs in Florida, Tennessee, New York,
Kentucky and Ohio. The combination of these policies may come together to have the
synergistic effect in reducing the opioid epidemic. In essence the future of the opiate epidemic
looks promising being that government attention along with community partnerships can make a
difference.
In future, utilizing genetic data may play a role in prevention or treatment strategies
related to addiction. As highlighted in this essay, our current understanding lacks clear and
important details regarding cellular mechanisms. However, further research may elucidate this
information, which could lead to new, possibly more effective means of treatment. Furthermore,
it may become commonplace to genotype individuals believed to be at higher risk of developing
27
a substance use disorder. As part of medical records, the use of a patient’s genetic data, their
genotype and family history, could be used to avoid detrimental medical decisions, such as
prescribing a highly addictive opioid pain reliever to a patient that has increased risk due to
genetic susceptibility of developing addiction. After understanding the nature of a person’s
genetic susceptibility, in accordance with their personality traits, behavior, and other
environmental factors, it may be possible to create health policy that takes these factors into
consideration. For example, future use of genetic data could serve as a mitigating factor in
sentencing of individuals charged with illicit drug use. A comprehensive approach from health
policy and personalized medicine could have the potential to reduce the prevalence of substance
use disorders over time. Herein lies the potential for genetics to advance public health.
28
APPENDIX: TABLES
Table 1. State Overdose Prevention Legislation Summary
Type of Legislation % of Jurisdictions (number)
Naloxone Overdose Prevention 90.1 (46)
Good Samaritan Overdose Protection 70.5 (36)
PDMP 98 (50)
Percentage of U.S. jurisdictions (50 states and District of Columbia) that include some form of Naloxone overdose prevention law, Good Samaritan overdose protection law, and enactedPrescription Drug Monitoring Programs (PDMP) as of 2016.
Table 2. Pennsylvania Compared to Selected States
State Naloxone(year effective)
Good Samaritan (year effective)
PDMP (year mandatory)
Pennsylvania Yes (2014) Yes (2014) Yes (N/A)Ohio Yes (2015) No Yes (2011)
Tennessee Yes (2015) Yes (2016) Yes (2013)
Kentucky Yes (2015) Yes (2015) Yes (2012)
Florida Yes (2016) Yes (2012) Yes (2010)
New York Yes (2016) Yes (2015) Yes (2013)
Pennsylvania compared to selected states that feature some form of Naloxone overdose prevention law, Good Samaritan overdose protection law, and enacted Prescription Drug Monitoring Programs as of 2016. States were selected based on their reported success, highlighted by the Centers for Disease Control and Prevention, in decreasing opioid prescriptions and/or doses dispensed following mandatory enrollment in a Prescription Drug Monitoring Program (PDMP).
29
BIBLIOGRAPHY
Allegheny County Health Department. (2015, May 21). Health Department Director Issues Standing Naloxone Order for Pharmacies. Retrieved October 25, 2016, from http://www.achd.net/pr/pubs/2015release/052115_nalaxone.html
Baker-Andresen, D., Zhao, Q., Li, X., Jupp, B., Chesworth, R., Lawrence, A. J., et al. (2015). Persisten variations in neuronal DNA methylation following cocaine self-administration and protracted abstincence in mice. Neuroepigenetics , 4, 1-11. doi: 10.1016/j.nepig.2015.10.001
Bosch, P. J., Benton, M. C., Macartney-Coxson, D., & Kivell, B. M. (2015). mRNA and microRNA analysis reveals modulation of biochemical pathways related to addiciton in the ventral tegmental area of methamphetamine self-administering rats. BMC Neuroscience , 16 (43). doi: 10.1186/s12868-015-0186-y
Brown, A. L., Flynn, J. R., Smith, D. W., & Dayas, C. V. (2011). Down-regulated stratial gene expression for synaptic plasticity-associated proteins in addiction and relapse vulnerable animals. International Journal of Neuropsychopharmacology , 14, 1099-1110. doi: 10.1017/S1461145710001367
Centers for Disease Control and Prevention (CDC). (2014, July 4). Decline in Drug Overdose Deaths After State Policy Changes – Florida, 2010-2012. Morbidity and Mortality Weekly Reports, 63 (26), 569-574. Retrieved on October 25, 2016 from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6326a3.htm
Centers for Disease Control and Prevention (CDC). (2014, July 4). Vital Signs: Variation Among States in Prescribing of Opioid Pain Relievers and Benzodiazepines – United States, 2012. Morbidity and Mortality Weekly Reports, 63 (26), 563-568. Retrieved on October 25, 2016 from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6326a2.htm?s_cid=mm6326a2_w
Centers for Disease Control and Prevention (CDC). (2016, January 1). Increases in Drug and Opioid Overdose Deaths: United States, 2000–2014. Morbidity and Mortality Weekly Reports, 64 (50), 1378-1382. Retrieved October 25, 2016 from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6450a3.htm?s_cid=mm6450a3_w.
Dart, R. C., Surratt, H. L., Cicero, T. J., Parrino, M. W., Severtson, S. G., Bucher-Bartelson, B., et al. (2015). Trends in Opioid Analgesic Abuse and Mortailty in the United States. New
30
England Journal of Medicine , 372, 241-8. http://www.nejm.org/doi/full/10.1056/NEJMsa1406143#t=article
Doura, M. B., & Unterwald, E. M. (2016). MicroRNAs Modulate Interactions between stress and risk for cocaine addicion. Frontiers in Cellular Neuroscience , 10 (125). doi: 10.3389/fncel.2016.00125
General Assembly of Ohio, 131st. House Bill 4. (2015)(enacted). Retrieved November 5, 2016 from https://www.legislature.ohio.gov/legislation/legislation-documents?id=GA131-HB-4
General Assembly of Pennsylvania. Senate Bill 1164, Act 139, (2014)(enacted). Retrieved November 5, 2016 from http://www.legis.state.pa.us/cfdocs/legis/li/uconsCheck.cfm?yr=2014&sessInd=0&act=139
General Assembly of Pennsylvania. Senate Bill 1180, Act 191, (2014)(enacted). Retrieved November 5, 2016 from http://www.legis.state.pa.us/CFDOCS/Legis/PN/Public/btCheck.cfm?txtType=PDF&sessYr=2013&sessInd=0&billBody=S&billTyp=B&billNbr=1180&pn=2393
Im, H., Hollander, J. A., Bali, P., & Kenny, P. J. (2010). MeCP2 controls BDNF expression and cocaine intake through homeostatic interactions with microRNA-212. Nature Neuroscience , 13 (9), 1120-1127. doi: 10.1038/nn.2615
Kasanetz, F., Deroche-Gamonet, V., Berson, N., Balado, E., Lafourcade, M., Manzoni, O., et al. (2010). Transition to addiction is associated with a persistent impairment in synaptic plasticity. Science , 328 (5986), 1709-1712. doi: 10.1126/science.1187801
Kenny, P. J. (2014). Epigenetics, microRNA, and addiction. Dialogues in Clinical Neuroscience , 16 (3), 335-344. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214176/
Knothe, C., Oertel, B. G., Ultsch, A., Kettner, M., Schmidt, P. H., Wunder, C., et al. (2016). Pharmacoepigenetics of the role of DNA methylation in mu-opioid receptor expression in different human brain regions. Epigenomics , Epub ahead of print. doi: 10.2217/epi-2016-0072
Lee, M., Silverman, S., Hansen, H., Patel, V., & Manchikanti, L. (2011). A comprehensive review of opioid-induced hyperalgesia. Pain Physician , 14, 145-161. http://www.painphysicianjournal.com/linkout?issn=1533-3159&vol=14&page=145
Legal Science, LLC. Policy Surveillance Program: A LawAtlas Project. Accessed October 18, 2016 from http://lawatlas.org.
Levran, O., Peles, E., Randesi, M., Li, Y., Rotrosen, J., Ott, J., et al. (2014). Stress-related genes and heroin addiction: a role for a functional FKBP5 haplotype. Psychoneuroendocrinology , 45, 67-76. doi: 10.1016/j.psyneuen.2014.03.017
31
Lewohl, J. M., Nunez, Y. O., Dodd, P. R., Tiwari, G. R., Harris, R. A., & Mayfield, R. D. (2011). Up-regulation of microRNAs in brain of human alcoholics. Alcoholism, Clinical and Experimental Research , 35 (11), 1928-1937. doi: 10.1111/j.1530-0277.2011.01544.x.
Liang, D., Li, X., & Clark, J. D. (2013). Epigenetic regulation of opioid-induced hyperalgesia, dependence, and tolerance in mice. Journal of Pain , 14 (1), 36-47. doi: 10.1016/j.jpain.2012.10.005.
Manolio, T. A., Collin, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., et al. (2009). Finding the missing heritability of complex diseases. Nature , 461 (8), 747-753. doi: 10.1038/nature08494
McAdams, T. A., Gregory, A. M., & Eley, T. C. (2013). Genes of experience: explaining the heritability of putative environmental variables through their association with behavioral and emotional traits. Behavioral Genetics , 43, 314-328. doi: 10.1007/s10519-013-9591-0
McCarthy, D. M., Brown, A. N., & Bhide, P. G. (2012). Regulation of BDNF expression by cocaine. Yale Journal of Biology and Medicine , 85 (4), 437-446. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3516887/
Most, D., Ferguson, L., & Harris, R. A. (2014). Molecular basis of alcoholism. Handbook of Clinical Neurology , 125, 89-111. doi: 10.1016/B978-0-444-62619-6.00006-9.
National Alliance for Model State Drug Laws. (2016, May). Compilation of Prescription Monitoring Program Maps. Retrieved October 25, 2016 from http://www.namsdl.org/library/CAE654BF-BBEA-211E-694C755E16C2DD21/
Nelson, E. C., Agrawal, A., Heath, A. C., Bogdan, R., Sherva, R., Zhang, B., et al. (2016). Evidence of CNIH3 involvement in opioid dependence. Molecular Psychiatry , 21 (5), 608-614. doi: 10.1038/mp.2015.102
OverdoseFree PA. (2016). Overdose Data: Allegheny County. Retrieved October 25, 2016, from https://www.overdosefreepa.pitt.edu/charts/
Palmer, R. H., Brick, L., Nugent, N. R., Bidwell, L. C., McGeary, J. E., Knopik, V. S., et al. (2015). Examining the role of common genetic variants on alcohol, tobacco, cannabis, and illicit drug dependence. Addiction , 110 (3), 530-537. doi: 10.1111/add.12815
Paulozzi, L. J., & Ryan, G. W. (2006). Opioid analgesics and rates of fatal drug poisoning in the united states. American Journal of Preventative Medicine , 31 (6), 506-511. doi: 10.1016/j.amepre.2006.08.017
Pennsylvania Department of Health. Standing Order DOH-002-2016 (2016). Retrieved November 5, 2016 from http://www.health.pa.gov/My%20Health/Diseases%20and%20Conditions/A-D/Documents/ACT%20139/Naloxone%20Standing%20Order%20DOH-General%20PublicMarch2016.pdf
32
Pietrzykowski, A. Z., Friesen, R. M., Martin, G. E., Puig, S. I., Nowak, C. I., Wynne, P. M., et al. (2008). Post-transcriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol. Neuron , 59 (2), 274-287. doi: 10.1016/j.neuron.2008.05.032.
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Top 10 replicated findings from behavioral genetics. Perspectives on Psychological Science , 11 (1), 3-23. doi: 10.1177/1745691615617439
Prescription Drug Monitoring Program Center of Excellence at Brandeis. (2016, May 3). PDMP prescriber use mandates: characteristics, current status, and outcomes in selected states. Retrieved October 25, 2016, from Brandeis University: http://www.pdmpassist.org/pdf/COE_documents/Add_to_TTAC/COE%20briefing%20on%20mandates%203rd%20revision.pdf
Quinn, R. K., Brown, A. L., Goldie, B. J., Levi, E. M., Dickson, P. W., Smith, D. W., et al. (2015). Distince miRNA expression in dorsal stratial subregions is associated with risk for addiction in rats. Translational Psychiatry , 5 (e503), 1-7. doi: 10.1038/tp.2014.144
Quinones, S. (2015). Dreamland: The True Tale of America's Opiate Epidemic. New York: Bloomsbury Press.
Russo, S. J., Mazei-Robison, M. S., Ables, J. L., & Nestler, E. J. (2009). Neurotrophic factors and structural plasticity in addiction. Neuropharmacology , 56, 73-82. doi: 10.1016/j.neuropharm.2008.06.059
Sahbaie, P., Liang, D., Shi, X., Sun, Y., & Clark, J. D. (2016). Epigenetic regulaiton of spinal cord gene expression contributes to enhanced postoperative pain and analgesic tolerance subsequent to continuous opioid exposure. Molecular Pain , 12, 1-11. doi: 10.1177/1744806916641950
Schmidt, H. D., McGinty, J. F., West, A. E., & Sadri-Vakili, G. (2013). Epigenetics and psychostimulant addiction. Cold Springs Harbor Perspectives in Medicine , 3, a012047. doi: 10.1101/cshperspect.a012047
Schuckit, M. A. (2016). Treatment of opioid-use disorders. New England Jounal of Medicine , 375, 357-368. doi: 10.1056/NEJMra1604339
Smith, J. L., Mattick, R. P., Jamadar, S. D., & Iredale, J. M. (2014). Deficits in behavioral inhibition in substance abuse and addiction: a meta-analysis. Drug and Alcohol Dependence , 145, 1-33. doi: 10.1016/j.drugalcdep.2014.08.009
Substance Abuse and Mental Health Services Administration. (2015). Key Substance Use and Mental Health Indicators in the United States: Results from the 2015 National Survey on Drug Use and Health. Retrieved December 5, 2016, from http://www.samhsa.gov/data/sites/default/files/NSDUH-FFR1-2015/NSDUH-FFR1-2015/NSDUH-FFR1-2015.pdf.
33
U.S. Senate, 114th Congress. Bill 524, Comprehensive Addiction and Recovery Act of 2016 (2016)(enacted). Retrieved November 5, 2016 from https://www.congress.gov/bill/114th-congress/senate-bill/524/text?q=%7B%22search%22%3A%5B%22comprehensive+addiction+and+recovery+act%22%5D%7D&r=1.
Wermeling, D. P. (2015). Review of naloxone safety for opioid overdose: practical considerations for new technology and expanded public access. Therapeutic Advances in Drug Safety , 6 (1), 20-31. doi: 10.1177/2042098614564776
Xu, L., Hong, Q., Chen, X., Xu, X., Liu, H., Zhou, W., et al. (2016). H4K5 histone acetylation of BRG1 is associated with heroin administration rather than addiction. Experimental and Therapeutic Medicine , 12, 1929-1933. doi: 10.3892/etm.2016.3517
Yang, C., Li, C., Kranzler, H. R., Farrer, L. A., Zhao, H., & Gelernter, J. (2014). Exploring the genetic architecture of alcohol dependence in african-americans via analysis of a genomewide set of common variants. Human Genetics , 133 (5), 617-624. doi: 10.1007/s00439-013-1399-8.
Zaaijer, E. R., Brunijel, J., Blanken, P., Hendriks, V., Koeter, M. W., Kreek, M. J., et al. (2014). Personality as a risk factor for illicit opioid use and a protective factor for illicit opioid dependence. Drug and Alcohol Dependence , 145, 101-105. doi: 10.1016/j.drugalcdep.2014.09.783
34
