Challenges in Managing Cancer Pain: The Role of the Oncology Pharmacist
FarahmandAzadeh_Summer2014
1.
2. 1
EXECUTIVE SUMMARY
Developing Addiction/Pain Management genotyping Test
AutoGenomics, Inc
Azadeh Farahmand
July 2014
Professional Masters Degree Program
Cal State University San Marcos
Genetic factors play a key role in addiction and pain but are generally not evaluated in clinical practice.
Some people who experience chronic pain are genetically predisposed to neurochemical deficiencies. A
greater susceptibility to Prescription Drug Dependence (PDD) has been seen in pain patients. Physicians
fail to control pain in roughly 60% of patients taking narcotic pain medication even as they increase the
dosage and potency. The goal of this project was the design and testing of an Addiction/Pain Management
(APM) genotyping test (Research Use Only) to be used as a screening tool for physicians to personalize
treatment. This assay is based on mutations which have been utilized not only in diagnosis but also in
individual treatment procedures. For PCR and ASPE (asymmetric primer extension) reactions, primers
were designed for 16 analytes and tested for their effectiveness in detecting mutations using the
AutoGenomics, Inc. assay format. Results from these experiments demonstrated that 15 out of 16 pairs
(wild/mutant types) of analytes worked. Only the DRD4 analyte lacked significant signals. Due to the
potential interference between the DRD4 and 5HT2A analytes redesigning the DRD4 forward and reverse
PCR primers will be considered. Following optimization, the APM test will be subjected to alpha testing.
Once completed, the assay should provide better information regarding patients’ pain management
andxmedication/drugxaddictionxthanxisxcurrentlyxavailable.
3. Developing Addiction/Pain Management genotyping Test
AutoGenomics, Inc
Azadeh Farahmand
July 2014
Faculty Advisors
Project Chair: Betsy Read. Ph.D.
Committee Member: Sajith Jayasinghe. Ph.D.
Committee Member: Sherman Chang. Ph.D.
Professional Science Masters
California State University, San Marcos
4. i
Table of Contents
Developing Addiction/Pain Management genotyping Test....................................................... i
List of Figures and Tables Layout ........................................................................................ ii
Acknowledgements.............................................................................................................. iii
EXECUTIVE SUMMARY ................................................................................................. iv
Introduction........................................................................................................................... 1
The 16 Human genes considered in these studies are as follows:..................................... 2
Specific aims of this project were as follows:................................................................... 5
METHODS & MATERIALS ............................................................................................... 7
PCR Formulation .............................................................................................................. 8
SAP-EXO.......................................................................................................................... 8
Allele Specific Primer Extension (ASPE) ........................................................................ 9
Hybrdization on Microarray Chips, Washing, and Reading........................................... 10
Results................................................................................................................................. 12
Discussion........................................................................................................................... 19
Future Direction.............................................................................................................. 22
References........................................................................................................................... 24
Appendix............................................................................................................................. 30
6. iii
Acknowledgements
I would like to thank my supervisor (Sherman Chang. Ph.D.), the program director (Betsy
Read. Ph.D.), the committee member (Sajith Jayasinghe. Ph.D.), and my colleagues (Jerome
Streifel. Ph.D. and Marsha Macdonald. B.S.) for their guidance in this project.
Above all, I want to send all my love to my heavenly kind parents (Flora Ashrafi and Reza
Farahmand.). They are not only impeccable parents, but also the greatest friends ever, without
whom there would be no motivation to walk this hard line. I should also thank my nice
grandmother and my dearest brother for encouraging me to go ahead.
Dedicated to:
My wonderful mother, wholeheartedly
7. iv
EXECUTIVE SUMMARY
Developing Addiction/Pain Management genotyping Test
AutoGenomics, Inc
Azadeh Farahmand
July 2014
Professional Masters Degree Program
Cal State University San Marcos
Genetic factors play a key role in addiction and pain but are generally not evaluated in
clinical practice. Some people who experience chronic pain are genetically predisposed to
neurochemical deficiencies. A greater susceptibility to Prescription Drug Dependence (PDD)
has been seen in pain patients. Physicians fail to control pain in roughly 60% of patients
taking narcotic pain medication even as they increase the dosage and potency. The goal of
this project was the design and testing of an Addiction/Pain Management (APM) genotyping
test (Research Use Only) to be used as a screening tool for physicians to personalize
treatment. This assay is based on mutations which have been utilized not only in diagnosis
but also in individual treatment procedures. For PCR and ASPE (asymmetric primer
extension) reactions, primers were designed for 16 analytes and tested for their effectiveness
in detecting mutations using the AutoGenomics, Inc. assay format. Results from these
experiments demonstrated that 15 out of 16 pairs (wild/mutant types) of analytes worked.
Only the DRD4 analyte lacked significant signals. Due to the potential interference between
the DRD4 and 5HT2A analytes redesigning the DRD4 forward and reverse PCR primers will
be considered. Following optimization, the APM test will be subjected to alpha testing. Once
completed, the assay should provide better information regarding patients’ pain management
andxmedication/drugxaddictionxthanxisxcurrentlyxavailable.
8. 1
Introduction
For many people, pain management is a prominent part of daily healthcare management.
More than 116 million people worldwide are struggling with acute or chronic pain derived
from injuries and neuropathic dysfunctions. This group consists mostly of the elderly, cancer
patients, injured athletes, and women suffering from obstetric pain (Centers for Disease
Control and Prevention. 2013). Pain is not adequately controlled in such people, even as
physicians increase the utilization and dosage of opioid/narcotic pain. In addition, many pain
patients fail medical detoxification and experience high relapse rates. Common pain-
management medications include hydrocodone (more than 131.9 million prescriptions filled
in 2010), codeine, oxycodone, and other opioids. When used correctly, these medications are
effective; however, they are potentially deadly when not used properly (Castro, M. 2006).
A patient’s genetics not only plays a key role in determining the efficacy and toxicity
of the drug being administered but is also vital in the dependency or physiologic
addiction to such medicines during long-term use. Research studies in the area of
pain management and addiction, have identified 16 genes that are important not only
in diagnosis, but in individual treatment procedures. In addition, mutations in some of
the genes correlated with a person’s predisposition to medication/drug addiction
(Allam et al., 2014).The patients’ genotype utilizing the APM test will determine
their response to treatment. It also helps physicians to mitigate the potential risks of
addiction associated with long-term opioid therapy.
Many of the genes linked with addiction have been identified in mice using the reward
cascade system. The brain reward cascade system (Figure1) initiates with serotonin and
involves dopamine (DA), endorphins, and gamma-aminobutyric acid (GABA). Feelings of
anxiety and anger can be exhibited if an imbalance exists in the system.
Figure 1. The brain reward cascade. Neurotransmitter activating the enkephalins (one type of
brain endorphin); the enkephalins are released in the hypothalamus and stimulate mu receptors. The
5HT2a Receptor Mu Opiote Receptor GABA Receptor Dopamine Neuron D2 Dopamine Receptor REWARD
Serotonin Enkephalin GABA Dopamine
9. 2
neurotransmitter GABA (an inhibitory neurotransmitter) stimulates GABA which stimulates dopamine
neurons and allow for just the right amount of dopamine to release.
The serotonergenic system in the hypothalamus leads to the stimulation of delta mu receptors
by serotonin, resulting in production of enkephalins. The enkaphalinergic system induces an
inhibition of the GABA transmission and allows for fine-tuning of GABA activity and the
normal release of dopamine at the reward site of the brain. When DA is released into the
synapse, it stimulates a number of DA receptors (D1-D5), which result in a state of well-
being. When there is a dysfunction in the brain reward circuitry or cascade, the brain requires
dopaminergic activation. This trait leads to drug-seeking behaviors. Alcohol and
psychostimulants such as cocaine, heroin, marijuana, nicotine, and glucose all result in
activation and neuronal release of DA. Several types of genes and Single Nucleotide
Polymorphisms (SNPs) in these genes have been correlated with addiction. Examples include
the A1 allele mutation of the DR receptor, which is more common in people addicted to
alcohol and cocaine, and the CYP2A6 gene mutation, which has been correlated to addiction
to cigarettes.
The 16 Human genes considered in these studies are as follows:
Serotonin 2a receptor (5HT2A, Chromosome 13): 5HT2A plays a role in modulating
normal physiological functions. It is a neurotransmitter that plays a role in
modulating mood states in particular. Studies have indicated that the 5HT2A
receptors play a role in neuropsychiatric cases, and the SNP rs7997012 has been
linked to various responses to antidepressant treatments (Prado Lima et al., 2004).
Serotonin-transporter-linked polymorphic region (5HTTLPR, Chromosome 17):
5HTTLPR gene, which codes for the serotonin transporter has been thoroughly
investigated in a number of behavioral, pharmacogenetic and genetics studies. The
polymorphism occurs in the promoter region of the gene, which contains two
variations: a short allele and a long allele. Studies have found that the long allele
results in higher serotonin transporter mRNA transcription in human cell line, and
this increase has been linked to the A-allele of SNP rs25531.(Kosek et al., 2009).
Catechol-O-Methyl Transferase (COMT, Chromosome 22): The COMT gene has
been linked with low COMT enzyme activity and high endogenous dopamine
synaptic levels in the prefrontal cortex. A study of 351 participants found
10. 3
associations between SNP rs4680 in the COMT gene and the ability to experience
reward. The reward experience increases with the number of alleles in which SNP
rs4680 exists (Hosak et al., 2006).
Dopamine D1 Receptor (DRD1, Chromosome 5): DRD1SNP rs4532 has been linked
with the severity of alcohol addiction in studies implementing the Alcohol Use
Disorders Identification Test (AUDIT) (Kim et al., 2007).
Dopamine D2 Receptor (DRD2, Chromosome 11): Association of the DRD2 with
severe alcoholism was shown in a recent multiple population study by the National
Institute on Alcohol Abuse and Alcoholism. These studies correlated the DRD2 gene
SNP rs1800497 with Substance Use Disorder (SUD) (Freire et al., 2006).
Dopamine D4 Receptor (DRD4, Chromosome 11): The DRD4 SNP rs3758653 plays
an important role in opioid dependence by the modulation of cold-pain responses.
Homozygous T/T individuals appear to have a higher tendency to use opioids
because they experience pain less strongly after chronic opioid use (Schinka &
Letsch 2002)
Dopamine Transporter (DAT, Chromosome 5): The DAT is linked to a number of
dopamine-related disorders, including attention deficit disorder (ADD), bipolar
disorder, and clinical depression. These disorders have been associated to SNP
rs56947 in the DAT gene (Vandenbergh et al., 1992).
Dopamine–beta-hydroxylase gene (DBH, Chromosome 1): DBH gene codes for the
enzyme dopamine beta (β)-hydroxylase responsible for converting dopamine to
norepinephrine. SNP rs1611115 in the DBH gene has been shown to be involved
with up to 50% of the (β)-hydroxylase enzymatic increase activity. An association
between this polymorphism and the performance of children and adolescents with
ADHD in neuropsychological measures of executive function (EF) has been made.
Therefore, physicians need to be cautious in prescribing psychiatric medications to
such patients (Kieling et al., 2008)
Methylene Tetrahydrofolate Reductase (MTHFR, Chromosome 1): MTHFR Gene has
been associated with prescription drug addiction. A link between the MTHFR SNP
rs1801133 and depression, schizophrenia, and bipolar disorder has been
demonstrated in various studies. Addiction research on homocysteine metabolism
11. 4
and its association with alcohol dependence has shown that plasma homocysteine
levels are influenced by the SNP rs1801133 (van Ede et al., 2001).
Human Kappa (κ) Opioid Receptor (OPRK1, Chromosome 8): The OPRK1 binds to
the peptide opioid dynorphin. κ receptors are widely distributed in the brain, spinal
cord, and in pain neurons. Studies have linked a higher frequency of the OPRK1 SNP
rs1051660 to heroin-dependent individuals as compared to control subjects. Thus,
this gene may be valuable to addiction diagnostics (Gerra et al.,2007).
Gamma-aminobutyric Acid (GABA, Chromosome 5): GABA, the main inhibitory
neurotransmitter in the mammalian central nervous system plays an important role in
regulating neuronal excitability within the nervous system. Cravings for alcohol and
food have been associated with this gene. SNP rs211014 of the GABA receptor has
been reported to be involved with alcohol dependence and over eating (Foster &
Kemp, 2006)
Mu opioid receptor Gene (OPRM1, chromosome 6): Numerous studieshave
examined OPRM1 polymorphisms and its association with opioid addiction. The
most extensively studied OPRM1 variant is SNP rs1799971. A recent study revealed
an overrepresentation of the G variant (as part of a haplotype) in regular smokers as
compared to non-smokers. These results suggest a potential contribution of this SNP
to addictive behavior (Tan et al., 2009)
Mu-Opioid Receptor Gene (MUOR, Chromosome 6): The Mu (µ) opioid receptors
are a class of opioid receptors with a high affinity for enkephalins and beta-endorphin
but a low affinity for dynorphins. Three well-characterized variants of the µ opioid
receptor have been identified, but the most important is shown to be MUOR SNP
rs9479757. The MUOR SNP rs9479757 is linked to tolerance for and dependence on
narcotics and opioid analgesics like morphine ( Chong et al., 2005)
Galanin (GAL, chromosome 11): Galanin is a 30-amino acid neuropeptide and linked
to panic and other anxiety disorders. It is distributed in the central as well as
peripheral nervous system and is involved in diverse behavioral functions including
the stress response. The GAL SNP (rs948854) is linked to behavioral effects of
opiates and opioid withdrawal. The minor allele (G) is correlated to severe anxiety
and a higher activity of the hypothalamic-pituitary-adrenal-axis (Beer et al., 2013).
12. 5
Delta opioid receptor (DOR/OPRD1, chromosome 1): The delta opioid receptor is
involved in analgesic effects of opioids and reward. In addition, it may play a role in
the development of opioid tolerance. The DOR SNP rs2236861 was associated with
opioid dependence in a European study population. A positive association of this
SNP with heroin dependence in an Australian study population was also noted
(Nelson et al., 2014).
P-glycoprotein (ABCB1, chromosome 7): The p-glycoprotein is part of the ATP
binding cassette transporter family. It functions as a multi-specific efflux pump
transporting endogenous compounds and drugs from the intracellular to the
extracellular brain domain. It may also play a critical role in the distribution of drugs,
including certain opioids. Different SNPs of the ABCB1 have been linked with the
level of expression of the p-glycoprotein. Studies on the SNP rs1045642 of the
ABCB1 gene have revealed that the T variant of this SNP is associated with impaired
function and expression of the p-glycoprotein (Beer et al., 2013).
AutoGenomics (AGI), a molecular diagnostics company, plans to introduce a novel
Addiction and Pain Management (APM) assay that will target the SNPs in the above-
listed genes. This assay will allow for the effective monitoring and treatment of pain,
which will not only increase the quality of life of patients but also result in cost savings
for the health care system. The inappropriate use of pain management drugs incurs $72.5
billion in wasted costs each year, while adverse-event prevention testing costs
approximately $500 per patient and $58 billion per year. It has been estimated that proper
testing can result in an annual savings of $14.5 billion to healthcare in the United States
(Centers for Disease Control and Prevention. 2013)
Specific aims of this project were as follows:
Designing primers for both Polymerase Chain Reaction (PCR) and Allele Specific
Primer Extension (ASPE) through primer-design techniques targeting genetic
variations relevant to pain management and addiction
Implementing oligonucleotides in the AGI APM assay with ultimate goal of
developing feasible diagnostics
13. 6
Random blood-extracted DNA samples (from the Coreill Institute for Medical Research)
were used in these studies. Dual levels of specificity were achieved by multiplex touchdown
PCR followed by ASPE on an automated INFINITI PLUS platform. Touchdown PCR, a
technique which is utilized to inhibit non-specific extension, has been used in this project.
PCR amplicons are then transferred into the INFINITI PLUS Analyzer where they serve as
templates for the ASPE reaction. During ASPE, the fluorescently labeled nucleotide dCTP is
incorporated. Subsequently, the fluorescently labeled ASPE extension products are captured
via hybridization onto the microarray chips. This hybridization is affected by the ASPE
primer’s Tag sequence annealing to the oligonucleotide capture probe on the microarray chip.
The INFINITI PLUS senses the intensity of the fluorescent signal being produced at specific
addresses on the microarray chip and coverts those signals to numeric values. The values are
the raw data, and the INFNITI PLUS makes a diagnostic call of positive based on ratio to the
negative signals. The negative signals are those that fall below a given cutoff for the
particular assay.
The microarray chip consists of multiple layers of porous hydrogel matrices ~8-10 µm thick
on a polyester solid base. This provides an aqueous microenvironment that is highly
compatible with biological materials. The second layer incorporates a proprietary
composition for removing most of the unbound fluorescence.
The goal of this project is to determine the feasibility of a multiplex molecular diagnostic test
for genetic biomarkers in the area of pain management and addiction, utilizing the automated
microarray technology developed by AGI. This assay is based on 16 mutations, involved in
human brain reward cascade, which have been utilized not only in diagnosis but also in
individual treatment procedures.
14. 7
METHODS & MATERIALS
Clinical Samples: Random blood-extracted DNA samples ordered from the Coreill Institute
for Medical Research were used in these studies. 10 to 50 nanograms of DNA were used per
reaction.
Primer Design: Target mutations were entered into the National Center for Biotechnology
Information database (http://www.ncbi.nlm.nih.gov) to obtain information on existing
mutations and Minor Allele Frequency in an approximately one killobase region. Primer3, an
online primer generating tool, was used to design both PCR and ASPE primers
(http://www.primer3.com). Several factors were considered in designing the primers: the melt
temperature (Tm; 58 C-70 C), G/C content (60%), and no extraneous mutations
(http://WWW.SNPcheck.org). In addition, PCR primers were required to amplify an
approximately 350 base-pair region. The ASPE primers were designed to incorporate specific
dGTP content complimentary to fluorescently labeled dCTP and a 5’ Tag region. Two types
of ASPE primers—wild type and mutant—were designed. The ASPE primer that extends
most efficiently during thermocycling—and consequently, produces relatively higher relative
fluorescence units (RFUs)—is deemed positive for that analyte, either as wild type or mutant.
Mutant types of ASPE primers are exactly the same as Wild Type except for the very last
base at the 3’ end.
200 micromolar Primer Reagents: A total of 64 designed primers were ordered from
Integrated DNA Technologies (www.idtdna.com). The lyophilized primers were diluted in
1X Tris-EDTA (1X TE) buffer, and their optical densities (O.D.s) at 260 nanometers were
measured. Based on these values, primers were diluted to yield a 200 micromolar
concentration.
PCR: Random Coriell samples were PCR amplified in order to test the specificity and
sensitivity of the designed PCR primers. PCR was optimized by altering various conditions:
annealing temperatures, cycling times, and the total number of cycles. Touchdown PCR was
utilized in this project to increase the efficiency and specificity of the reaction. Multi-step
Touchdown temperature cycling conditions were employed to generate specific targets. PCR
15. 8
amplicons were analyzed by agarose-gel electrophoresis in order to make sure that the
designed primers were working properly.
PCR Formulation
Titanium Buffer: Since the template DNA’s phosphate is the substrate for the polymerase
enzyme, the presence of any other source of phosphate (P) may cause cross reactivity.
Therefore, a non-phosphate 10X buffer containing magnesium chloride (MgCl2) was used in
both the PCR and ASPE reactions.
Deoxynucleotide Triphosphates (dNTPs): The PCR reaction contains specific concentrations
of dNTPs to optimize assay performance in both the PCR and ASPE steps.
Dimethyl Sulfoxide (DMSO): DMSO binds to DNA at cytosine resides thereby lowering the
PCR annealing temperature of G/C-rich regions and facilitating the annealing of primers to
the template.
7-deaza-2 -deoxyguanosine-5 -triphosphate (7-deaza-dGTP): 7-deaza-dGTP is another PCR
enhancer, which is a modified -deoxyguanosine-5 -triphosphate (dGTP). This PCR enhancer
facilitates the annealing of primers to template. In DNA G/C bond requires higher melting
temperature than A/T regions. 7-deaza-dGTP is a modified dGTP analog that lacks a nitrogen
molecule at the seven position of the purine ring. The absence of this nitrogen destabilizes G-
quadruplex formation. This reduces the strength of G/C-rich duplexes and thus lowers the
melting temperature.
Polymerase I (Titanium Taq): Titanium Taq is a highly robust, sensitive, hot-start DNA
polymerase (Clontech Laboratoriea).
SAP-EXO
Shrimp Alkaline Phosphatase (SAP): SAP is an enzyme that dephosphorylates dNTPs. The
addition of SAP prevents the incorporation of dNTPs in the downstream ASPE reaction—this
is particularly important in terms enhancing efficient incorporation of DyLight -dCTP. The
SAP (1 unit/ l) employed was in a storage buffer containing 25mM Tris-HCL, pH 7l.5; 1
mM MgCl2; and 50% glycerol (Affymetrix).
16. 9
Exonuclease (EXO): EXO is an enzyme that degrades any unincorporated primers prior to the
ASPE reaction. The EXO employed was in a storage buffer containing 20mM Tris-HCl, pH
7.5; 0.5 mM EDTA; 5 mM Beta-ME; and 50% glycerol (Affymetrix).
SAP-Exo Reactions: Following PCR, the samples were treated with SAP-EXO in order to
prevent end-labeling of primers, to degrade all unincorporated single-strand DNAs, and to
dephosphorylate any unincorporated dNTPs. The SAP-Exo step, prior to the ASPE reaction,
is critical to avoid possible involvement of residual primers or dNTPs from the PCR product
during ASPE extension.
The following steps performed in the INFINITI PLUS Analyzer:
Figure 2. AGI INFINITI PLUS Analyzer.
Allele Specific Primer Extension (ASPE)
ASPE: Once a PCR amplicon containing allele-specific target regions has been generated, it
is then utilized as a template in the ASPE reaction. The ASPE primers contain a Tag
sequence at their 5’ end that can then hybridize with a capture probe attached to the
microarray chip (Biofilm Chip; AGI). Once hybridized, the ASPE extension product—
containing incorporated DyLight-dCTP—generates a signal (Relative Fluorescent Unit; RFU)
that can be detected by the INFINITI PLUS Analyzer.
ASPE Formulation: The ASPE reaction contains d(AGT)TPs and Cy5-dCTP (Dylight 649 ),
a fluorescently labeled dCTP that is detected then by the analyzer.
17. 10
Hybridization Buffer (HYB): The Hybridization Buffer is added to increase the volume of the
PCR reaction to ensure complete coverage of the chip’s surface. It also provides the optimal
salt concentration to achieve correct stringency.
HYB Control spot (Figure 3): The HYB Control spot binds the hybridization control,
which is a DyLight-labeled oligonucleotide. The presence of a signal on this spot
indicates that pipetting and hybridization on the microarray chip was performed
correctly.
BKGD spot (Figure 3): The BKGD spot detects any nonspecific binding of the
labeled ASPE primers. It is also used to correct the signals from the analyte spots for
nonspecific binding and for washing variations.
Cy5 Registration spot (Figure 3): The Cy5 Registration spot is used to correct for
positional variation of the array.
Hybrdization on Microarray Chips, Washing, and Reading
Following the ASPE step, reaction products were hybridized on microarray chips and
washed: the Infiniti Plus dispensed 80 microliters of HYB into each PCR plate well; the
tubes’ contents (120 μl) were then mixed and aliquoted onto the microarray chips; the
microarray chips were then incubated for 90 minutes at 40°C. Following hybridization, the
Infiniti Plus Analyzer washed the chips and read the RFUs.
18. 11
Figure 3. AGI Microarray Chip Map. Each capture probe has three spots located in three different zones: Safe
zone, Intermediate zone, and High risk zone.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
80 22 2 93 84 70
MCOLN-
d6.4-W
GBA394-
M
GBA370-
W
NP496-M FA322-M ASPA433-
W High risk zone spots
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
85 16 90 32 16 18
GBA496-
M
BKGD TS249-W GBAd55-
M
BKGD ASPA231-
M-A Intermidiate Zone spots
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
3 63 12 33 60 53
GBA409-
M
TS269-W TS249-M GBAd55-
W
ASPA305-
W
FAIVS4-
W Safe Zone spots
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
45 61 94 40 80 35 30 82 19 89
ASPA433-
M
TS269-M FD696-W MCOLN-
d6.4-M
MCOLN-
d6.4-W
ML-In3-M ML-In3-W GBA84-M ASPA285-
M
FA322-W
Tip Landing Zone
76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
7 46 71 101 23 93 65 34 28 86 99 56 81 20 72
BLM2281-
W
TS1278-
W
FD696-M TSd7.6K NP608-W NP496-M NP496-W NP330-M NP330-W NP302-M NP302-W TSIn12-M GBA84-W ASPA285-
W
FAIVS4-
M Cy5 Probes spots
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105
4 97 8 10 24 14 3 31 22 29 1 98 13 23 71 HYBC spots
BLM2281-
M
TS1278-M FDIn20-W HYBC NP608-M GBA444-
W
GBA409-
M
GBA409-
W
GBA394-
M
GBA394-
W
FDIn20-M TSIn12-W GBAIVS2-
M
NP608-W FD696-M
106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
87 83 1 11 89 5 20 15 18 6 8 36 25 35 94
TS247-W TSIn9-W FDIn20-M ASPA231-
W-C
FA322-W GBA444-
M
ASPA285-
W
ASPA231-
M-T
ASPA231-
M-A
GBA370-
M
FDIn20-W TSIn9-M GBAIVS2-
W
ML-In3-M FD696-W
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
43 36 99 18 84 91 19 16 11 2 71 83 85 56 24
TS247-M TSIn9-M NP302-W ASPA231-
M-A
FA322-M GBA496-
W
ASPA285-
M
BKGD ASPA231-
W-C
GBA370-
W
FD696-M TSIn9-W GBA496-
M
TSIn12-M NP608-M
136 137 138 139 140 141 142 143 144 145 146 147 148 149 150
31 98 86 15 53 85 60 62 70 45 94 97 91 10 62
GBA409-
W
TSIn12-W NP302-M ASPA231-
M-T
FAIVS4-
W
GBA496-
M
ASPA305-
W
ASPA305-
M
ASPA433-
W
ASPA433-
M
FD696-W TS1278-M GBA496-
W
HYBC ASPA305-
M
151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
12 56 28 20 72 25 13 81 82 33 32 46 5 101 11
TS249-M TSIn12-M NP330-W ASPA285-
W
FAIVS4-
M
GBAIVS2-
W
GBAIVS2-
M
GBA84-W GBA84-M GBAd55-
W
GBAd55-
M
TS1278-
W
GBA444-
M
TSd7.6K ASPA231-
W-C
166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
63 30 34 19 7 4 87 43 90 12 63 61 14 40 46
TS269-W ML-In3-W NP330-M ASPA285-
M
BLM2281-
W
BLM2281-
M
TS247-W TS247-M TS249-W TS249-M TS269-W TS269-M GBA444-
W
MCOLN-
d6.4-M
TS1278-
W
181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
32 35 65 60 62 70 45 2 6 29 22 31 3 80 36
GBAd55-
M
ML-In3-M NP496-W ASPA305-
W
ASPA305-
M
ASPA433-
W
ASPA433-
M
GBA370-
W
GBA370-
M
GBA394-
W
GBA394-
M
GBA409-
W
GBA409-
M
MCOLN-
d6.4-W
TSIn9-M
196 197 198 199 200 201 202 203 204 205 206 207 208 209 210
81 16 93 23 24 89 84 53 72 7 4 87 43 16 90
GBA84-W BKGD-U NP496-M NP608-W NP608-M FA322-W FA322-M FAIVS4-
W
FAIVS4-
M
BLM2281-
W
BLM2281-
M
TS247-W TS247-M BKGD-U TS249-W
211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
13 33 91 8 83 97 29 61 25 99 14 5 15 34 65
GBAIVS2-
M
GBAd55-
W
GBA496-
W
FDIn20-W TSIn9-W TS1278-M GBA394-
W
TS269-M GBAIVS2-
W
NP302-W GBA444-
W
GBA444-
M
ASPA231-
M-T
NP330-M NP496-W
226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
6 98 82 30 86 1 40 101 10 28
19. 12
Results
The purpose of the first experiment was to determine the optimum PCR temperature. A
theoretical calculation, utilizing PCR Stoichiometry software, was also performed to
determine the optimal concentration of primers and dNTPs. This software calculates the
optimal amount of dNTPs in the PCR reaction based on the generated amplicons. Using the
spatial temperature gradient function of the thermocycler, eight different PCR Annealing
Temperatures (Ta) were compared in one run for two different Coriell samples. The reactions
contained all 16 pairs of PCR primers described in the introduction. Results are shown in
Figure 4. Some of the signals were at acceptable levels. The optimum PCR Ta was 60°C for
analytes with good signals. It should be noted that the non-responding analytes failed in both
random Coriell samples which indicating non-sample related issues.
Figure 4. Optimum PCR Annealing Temperature. (Analytes with no data did not generate sufficient signals.)
To address the above-mentioned weak signals, a PCR primer titration experiment was
conducted. Various primer concentrations (25-400 nM) were examined. The same conditions
were also tested, with and without the addition of DMSO. No improvements were seen with
the addition of DMSO. A non-template or negative control (1XTE Buffer) was included in
this experiment. For the analytes that had good signals, the optimum PCR primer
concentrations was 100 nM.(Figure5).
51
53
55
57
59
61
63
65
T.aOptimum(ºC)
Analytes
Optimum PCR Annealing Temprature
20. 13
Figure 5. Optimum PCR Primer Concentrations. (Analytes with no data did not generate sufficient signals.)
A matrix of two variables, PCR temperature profile (Figure 6) and SAP-EXO treatment, were
used in the next experiment. This resulted in acceptable signals for six of the nine previously
weak/non-responding analytes. An acceptable signal is defined by the following: the ratio of
analyte signal (the average of three spots) to BKGD signal (the average of three spots) plus
3σ of the BKGD spots. All of the analytes except HTTLPR, DRD2, and DRD4 yielded
acceptable signals under the new PCR temperature profile. The optimum PCR Ta was 64.9°C
(Figure 8). It should be noted PCR reactions at the Ta of 65.9°C, 66.9°C, and 67.9°C did not
yield acceptable signals. Compared to PCR reactions that yielded acceptable signals, the non-
SAP-EXO treatment resulted in false positives (Table 1).
Figure 6. PCR Temperature Profile. X axis shows the total 40 PCR cycles. The Y axis shows the PCR
temperature.
20
95
170
245
320
395
PCRPrimerConcnetration(nM)
Analytes
Optimum PCR Primer Concentrations
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60
Temperature(°C)
Minutes
PCR Temprature Profile
22. 15
Figure 7. Optimum PCR Annealing Temperature. (Analytes with no data did not generate sufficient signals.)
To further address the weak signals, monoplex verses multiplex PCR primer experiments
were conducted (Table 2). Monoplex PCR amplicons were run on an agarose gel, but no gel
bands were evident. Positive control analytes yielded high RFUs, but only one out of two
generated product bands. This indicates that the presence of a product band on a gel is not a
good predictor for RFU generation. No gels were run for the multiplex PCR reactions due to
overlapping gel bands.
Table 2. Composition of various PCR tubes in single and multiplex assays. Each tube contains different
52
54.5
57
59.5
62
64.5
67
69.5
72
T.aOptimum(ºC)
Analytes
Optimum PCR Annealing Temprature
PCR Reactions PCR Primer Mix ASPE Primer Mix Signals
(RFU)
PCR Reaction1 5HTTLPR 5HTTLPR 1
PCR Reaction2 DRD1 DRD1 130293
PCR Reaction3 DRD2 DRD2 1
PCR Reaction4 DRD4 DRD4 1
PCR Reaction5(Positive
Ctrl)
OPRK1 OPRK1 4263
PCR Reaction6 5HTTLPR, DRD2,
DRD4
5HTTLPR, DRD2,
DRD4
1
PCR Reaction7(1XTE) All the 16 PCR Primers All the 16 ASPE
Primers
~500
23. 16
The DNA target sequences were found to have a high G/C content for these three mutations.
Therefore, a matrix of two variables, PCR enhancers (DMSO and 7-deaza-dGTP) and PCR
Ta (59.9°C verses 64.9°C) were tested. The experiment included two different Coriell DNAs
and a negative control (1X TE buffer). These samples were all tested under six different
conditions (Table 2). Analyte 2HTTLPR yielded acceptable signals in the presence of 50% 7-
deaza-dGTP, whereas DRD2 and DRD4 did not yield acceptable signals under any of the
conditions tested. Negative control samples were run on an agarose gel. Some product bands
were seen for the negative control at 59.9°C and 65°C Ta; however, at 65°C, false positive
signals were tenfold less than those observed at 59.9°C.
Table 3, Six different assay conditions in presence of two different PCR enhancers, DMSO and 7-deaza-dGTP
Inclusion of redesigned primers for DRD2 resulted in the generation of an acceptable signal;
whearas, the inclusion of redesigned primers for DRD4 did not generate an acceptable signal.
Furthermore, under these conditions, the signal generated from the 5HT2A analyte—which
seemed robust enough in previous experiments—dropped dramatically. Also, under these
conditions, the signal generated from the DBH analyte dropped by roughly half. To address
these weak signals, an ASPE temperature-titration experiment was conducted. Eight different
ASPE annealing temperatures were compared in one run for two different Coriell samples.
The signals did not improve for the three weak analytes. The optimum annealing temperature
for the other analytes was determined to be 57°C (Figure9).
Modifiers Mix1 Mix2 Mix3 Mix4 Mix5 Mix6
DMSO(PCR) 0% 0% 5% 5% 10% 10%
DMSO(ASPE) 0% 0% 5% 5% 10% 10%
7-deaza-dGTP(PCR) 0% 50% 0% 50% 0% 50%
24. 17
Figure 8. Optimum ASPE Annealing Temperature. (Analytes with no data did not generate sufficient signals.)
The next experiment was performed to determine the effect of the PCR primer concentrations
on the weak DBH, 5HT2A, and DRD4 analytes. Four different Coriell samples were tested in
this experiment. The deficient analyte signals did not improve under any of the primer
concentrations tested. The optimum PCR primer concentration for most of the other analytes
was found to be 100 nM, with the exception of DBH which had an optimal primer
concentration of 150nM (Figure 10).The remaining two weak analytes, DRD4 and 5HT2A,
were run by themselves in the next experiment. DRD4 did not generate signal; however,
5HT2A generated acceptable signals. However, when running both 5HT2A and DRD4 in the
multiplex assay along with the 14 other analytes, no RFUs were generated for 5HT2A and
DRD4. When running only 5HT2A in the assay along with the other 14 analytes acceptable
RFUs were generated for 5HT2A; whereas, when running only DRD4 with the other 14
analytes resulted in non-acceptable RFUs for DRD4 (Figure11).
51
53
55
57
59
61
63
65
T.aOptimum(ºC)
Analytes
Optimum ASPE Annealing Temprature
25. 18
Figure 9. DBH PCR Primer Titration. DBH signals were increased by reducing the other 13 PCR Primer
concentrations to 50nM.
Figure 10. DRD4 PCR Primer Titration. DRD4 showed acceptable signals in none of the primer concentrations.
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
Sample1 Sample2 Sample3 Sample4
13 PCR Primer=50nM + DBH=100nM
RFU
PCR Primer Titration
DBH-C
DBH-T
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Sample1 Sample2 Sample3 Sample4 Sample1 Sample2 Sample3 Sample4 Sample1 Sample2 Sample3 Sample4
13 PCR Primer=50nM+ 5HT2A=100nM 13 PCR Primer=50nM+ DRD4=100nM 13 PCR Primer=50nM+ 5HT2A,
DRD4=100nM
RFU
PCR Primer Titration
DRD4-T
DRD4-C
26. 19
Discussion
The Institute of Medicine considers it standard care for physicians to offer testing for
medication addiction and pain management. There are two aspects of pain management to
consider when testing: pharmacokinetics (the process by which a drug is absorbed,
distributed, metabolized, and eliminated by the body) and pharmacodynamics (the action or
effects of drugs on living organisms). AGI has developed several assays— including
CYP450, 2B6, 2D6, 2C9, 2C19, CYP450, 3A5, and 3A4—which address the
pharmacokinetic aspect of pain management. Introducing the APM assay provides a
comprehensive screening tool for physicians to cover the other aspect of pain management
and addiction−pharmacodynamics.
For the APM assay development, PCR and ASPE primers were designed for 16 analytes and
tested for their effectiveness in detecting mutations using the AGI assay format. The first sets
of experiments were performed to optimize the PCR interim conditions with regard to PCR
annealing temperature and PCR primer concentrations. Most of the analytes did not generate
acceptable signals. Therefore, the ratio of actual to needed dNTPs was checked. There were
insufficient amounts of dNTPs in the 200nM and 400nM PCR primer concentration reactions.
At these two elevated PCR primer concentrations, not all the PCR primers had an equal
opportunity to amplify the target region due to inadequate amounts of dNTPs. The
elimination of SAP-EXO treatment on signal intensity was also tested, and it was determined
that without SAP-EXO signals were 30 percent lower on average. Of the 16 analytes tested,
only three failed to yield acceptable signals. Consequently, a series of experiments aimed at
finding conditions under which these analytes would generate acceptable signals were
conducted. Conditions were re-optimized, and the addition of DMSO and 7-deaza-dGTP
were investigated. Inclusion of 7-deaza-dGTP resulted in the generation of a good signal in
one of the three analytes; however, no signal was generated for the other two analytes. Due to
their potential cross reactivity, the PCR and ASPE primers from these two analytes were
examined for unexpected mismatches. The potential for mismatches was investigated
regarding not only the 16 sets of PCR and ASPE primers but also with respect to the 32
generated amplicons (using the software “Primer Potential Mismatches”). This software
calculated the number of contiguous mismatches against 3’ regions of amplicons and primers
(Figure14). Although the software did not recognize any significant mismatches, alternate
27. 20
PCR primer pairs generated from the primer3 program were selected for further studies.
Since the APM assay is a multiplexing panel, there is the potential of primer-dimer
formations, which may explain the unacceptable results generated from the two other
analytes. The Primer3 program is designed to select primers highly active in amplification,
but not all of these selections may lead to the generation of high RFU signals. Redesigned
primers for the DRD2 analyte produced a high signal; however, the DRD4 analyte did not
generate an acceptable signal. Furthermore, under these conditions, the signal generated from
the DBH analyte dropped by roughly half, while the 5HT2A analyte fell dramatically.
Experiments were then conducted to search for conditions under which the signals generated
by DBH, 5HT2A and DRD4 would be boosted by optimizing ASPE annealing temperature
and PCR primer concentrations.
The current optimized conditions (Appendix; Table 4.) are as follows:
50% 7-deaza-dGTP in the PCR amplification mix
SAP-Exo treatment
64.9ºC PCR annealing temperature
57ºC ASPE annealing temperature
100 nM ASPE primer concentrations
Optimal PCR primer concentrations in progress
Temperature profiles: PCR (Figure 6), SAP-Exo (Appendix; Figure 15), and ASPE
(Figure 12)
Figure 11. ASPE Temperature Profile.
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90
Temperature(°C)
Minutes
ASPE Temprature Profile
29. 22
Future Direction
Optimization will be continued in order to make the assay as robust as possible. In addition,
the potential use of Primestar GXL DNA Taq polymerase (http://www.takara-bio.com)—a
high functioning enzyme at G/C-rich regions—will be evaluated due to the existence of four
G/C-rich regions in the PCR amplicons relevant to DRD1, DRD2, DRD4, and DAT analytes.
Moreover, due to the lack of proper positive controls, a greater number of samples will be
processed to determine whether the low signals of 5HT2A, DRD4, and DBH are true
negatives or whether they are low due to the need for further assay optimization.
Following optimization, the APM assay will be subjected to alpha testing. This process will
be conducted with collaborators at the **** Psychiatry Department and the **** Center for
Alcoholism and Addiction. The alpha test trials will test the occurrence or absence of
mutations in a large number of control groups. Personalized Dx lab, a clinical diagnostic lab,
will validate the AGI APM test on more than 300 patient samples in the non-control group.
AGI will take steps to identify principal investigators both domestically and internationally
for additional sample resources. In this way, AGI will not only provide premarketing for the
test, but will also validate the assay. Although the APM assay is a Research Use Only (RUO)
test, validation of the test for Certification Export marking and potential 510k submission will
also be conducted.
The assay is still in the development phase; but when completed, it should provide better
information regarding patients’ pain management and medication/drug addiction than is
currently available. Table3 gives an example of the APM detail test report, which will be
provided to physicians. Based on the following report, a normal genotype would score as a
“low risk,” a heterozygote mutant as a “medium risk,” and a homozygote mutant as a “high
risk.” Thus, a person scoring positive for five out of 16 analytes would be expected to have a
better prognosis compared to a person scoring positive for 11 out of 16 analytes. In addition,
physicians will be cautious in prescribing medications to a person showing mutations. Such
patients are at a higher risk of dependency and toxicity to medications with prolonged use.
Collecting additional genotyping information utilizing AGI’s drug-metabolizer assays (as
listed above) in conjunction with the APM assay will direct physicians to better treatment
procedures.
30. 23
Table4; APM detail test report. W stands for Wilde type genotyping. M stands for Mutant type genotyping. H
stands for Heterozygous genotyping (Analysis will be done by comparing the ratio of the wild over the wild plus
mutant signals. Correction will be
Analyte Analysis
5-HT2A (rs7997012)
1 5-HT2A-C
2 5-HT2A-T
W
5-HTTLPR (rs25531)
3 5-HTTLPR-A
4.5-HTTLPR-G
W
COMT (rs4680)
5 COMT-G
6 COMT-A
W
DRD1 (rs4532)
7 DRD1-A W
8 DRD1-G
W
DRD2 (rs1800497)
9 DRD2-G
10 DRD2-A
H
DRD4 (rs3758653)
11 DRD4-T
12 DRD4-C
No_Call
DAT1 (rs6347)
13 DAT1-A
14 DAT1-G
W
DBH (rs1611115)
15 DBH-C
16 DBH-T
H
MTHFR (rs1801133)
17 MTHFR-C
18 MTHFR-T
H
OPRK1 (1051660)
19 OPRK1-G
20.OPRK-T
H
GABA (rs211014)
21 GABA-C
22 GABA-A
H
OPRM1 (rs1799971)
23 OPRM1-A
24 OPRM1-G
M
MUOR (9479757)
25 MUOR-G
26 MUOR-A
W
GAL (rs948854)
27 GAL-T
28 GAL-C
W
DOR (rs2236861)
29 DOR-G W
30 DOR-A
W
ABCB (rs1045642)
31 ABCB1-C
32 ABCB1-T
W
31. 24
References
Beer, B., Erb, R., Pavlic, M., Ulmer, H., Giacomuzzi, S., Riemer, Y., & Oberacher, H (2013).
Association of polymorphisms in pharmacogenetic candidate genes (OPRD1, GAL, ABCB1,
OPRM1) with opioid dependence in European population: A case-control study. PloS
one, 8(9), e75359.
Blum, K., Chen, A. L., Giordano, J., Borsten, J., Chen, T. J., Hauser, M., ... & Barh, D
(2012). The addictive brain: all roads lead to dopamine. Journal of psychoactive drugs, 44(2),
134-143.
Frackman, S., Kobs, G., Simpson, D., & Storts, D (1998). Betaine and DMSO: enhancing
agents for PCR. Promega notes, 65(27-29), 27-29.
AL-Eitan, L. N., Jaradat, S. A., Hulse, G. K., & Tay, G. K (2012). Custom genotyping for
substance addiction susceptibility genes in Jordanians of Arab descent. BMC research
notes, 5(1), 497.
Olsen, Y., & Daumit, G. L (2002). Chronic pain and narcotics. Journal of general internal
medicine, 17(3), 238-240.
Payne, K., & Shabaruddin, F. H (2010). Cost-effectiveness analysis in
pharmacogenomics. Pharmacogenomics, 11(5), 643-646.
Widengren, J., & Schwille, P (2000). Characterization of photoinduced isomerization and
back-isomerization of the cyanine dye Cy5 by fluorescence correlation spectroscopy. The
Journal of Physical Chemistry A, 104(27), 6416-6428.
Gruber, H. J., Hahn, C. D., Kada, G., Riener, C. K., Harms, G. S., Ahrer, W., ... & Knaus, H.
G (2000). Anomalous fluorescence enhancement of Cy3 and Cy3. 5 versus anomalous
fluorescence loss of Cy5 and Cy7 upon covalent linking to IgG and noncovalent binding to
avidin. Bioconjugate chemistry, 11(5), 696-704.
Musso, M., Bocciardi, R., Parodi, S., Ravazzolo, R., & Ceccherini, I (2006). Betaine,
dimethyl sulfoxide, and 7-deaza-dGTP, a powerful mixture for amplification of GC-rich
DNA sequences. The Journal of Molecular Diagnostics,8(5), 544-550.
Nestler, E. J., & Aghajanian, G. K (1997). Molecular and cellular basis of
addiction. science, 278(5335), 58-63.
Kalivas, P. W., & Volkow, N. D (2005). The neural basis of addiction: a pathology of
motivation and choice. American Journal of Psychiatry, 162(8), 1403-1413.
Nestler, E. J (2000). Genes and addiction. Nature genetics, 26(3), 277-281.
Goldman, D., Oroszi, G., & Ducci, F (2005). The genetics of addictions: uncovering the
genes. Nature Reviews Genetics, 6(7), 521-532.
Kreek, M. J., Nielsen, D. A., Butelman, E. R., & LaForge, K. S (2005). Genetic influences on
impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and
addiction. Nature neuroscience, 8(11), 1450-1457.
32. 25
Khoury, M. J., McCabe, L. L., & McCabe, E. R (2003). Population screening in the age of
genomic medicine. New England Journal of Medicine, 348(1), 50-58.
TRESCOT, A. M (2013). Genetic Testing in Pain Medicine. Rheumatology, 30.
Rounsaville, B. J., Kosten, T. R., Weissman, M. M., Prusoff, B., Pauls, D., Anton, S. F., &
Merikangas, K (1991). Psychiatric disorders in relatives of probands with opiate
addiction. Archives of General Psychiatry, 48(1), 33-42.
Crits-Christoph, P., Siqueland, L., Blaine, J., Frank, A., Luborsky, L., Onken, L. S., ... &
Beck, A. T (1999). Psychosocial treatments for cocaine dependence: National Institute on
Drug Abuse collaborative cocaine treatment study.Archives of general psychiatry, 56(6), 493-
502.
Volkow, N. D (2006). National Institute on Drug Abuse. Corsini Encyclopedia of
Psychology.
Fishbain, D. A., Fishbain, D., Lewis, J., Cutler, R. B., Cole, B., Rosomoff, H. L., &
Rosomoff, R. S (2004). Genetic testing for enzymes of drug metabolism: does it have clinical
utility for pain medicine at the present time A structured review. Pain Medicine, 5(1), 81-93.
Levran, O., Yuferov, V., & Kreek, M. J (2012). The genetics of the opioid system and
specific drug addictions. Human genetics, 131(6), 823-842.
Kandel, D., Chen, K., Warner, L. A., Kessler, R. C., & Grant, B (1997). Prevalence and
demographic correlates of symptoms of last year dependence on alcohol, nicotine, marijuana
and cocaine in the US population. Drug and alcohol dependence, 44(1), 11-29.
Dunn, K. M., Saunders, K. W., Rutter, C. M., Banta-Green, C. J., Merrill, J. O., Sullivan, M.
D., ... & Von Korff, M (2010). Opioid Prescriptions for Chronic Pain and OverdoseA Cohort
Study. Annals of Internal Medicine, 152(2), 85-92.
Sehgal, N., Manchikanti, L., & Smith, H. S (2012). Prescription opioid abuse in chronic pain:
a review of opioid abuse predictors and strategies to curb opioid abuse. Pain
Physician, 15(3), ES67-ES92.
Dervieux, T., Meshkin, B., & Neri, B (2005). Pharmacogenetic testing: proofs of principle
and pharmacoeconomic implications. Mutation Research/Fundamental and Molecular
Mechanisms of Mutagenesis, 573(1), 180-194.
Herman, A. I., & Balogh, K. N (2012). Polymorphisms of the serotonin transporter and
receptor genes: susceptibility to substance abuse. Substance abuse and rehabilitation, 3, 49.
Amass, L., Ling, W., Freese, T. E., Reiber, C., Annon, J. J., Cohen, A. J., ... & Horton, T
(2004). Bringing Buprenorphine‐Naloxone Detoxification to Community Treatment
Providers: The NIDA Clinical Trials Network Field Experience. The American Journal on
Addictions, 13(S1), S42-S66.
Fudala, P. J., Bridge, T. P., Herbert, S., Williford, W. O., Chiang, C. N., Jones, K., ... &
Tusel, D (2003). Office-based treatment of opiate addiction with a sublingual-tablet
formulation of buprenorphine and naloxone. New England Journal of Medicine, 349(10),
949-958.
33. 26
Jha, P., Chaloupka, F. J., Moore, J., Gajalakshmi, V., Gupta, P. C., Peck, R., ... & Zatonski,
W (2006). Tobacco addiction.
Walwyn, W. M., Miotto, K. A., & Evans, C. J (2010). Opioid pharmaceuticals and addiction:
the issues, and research directions seeking solutions. Drug and alcohol dependence, 108(3),
156-165.
Miotto, K., McCann, M. J., Rawson, R. A., Frosch, D., & Ling, W (1997). Overdose, suicide
attempts and death among a cohort of naltrexone-treated opioid addicts. Drug and alcohol
dependence, 45(1), 131-134.
Freese, T. E., Miotto, K., & Reback, C. J (2002). The effects and consequences of selected
club drugs. Journal of substance abuse treatment,23(2), 151-156.
Ehlers, C. L., Lind, P. A., & Wilhelmsen, K. C (2008). Association between single nucleotide
polymorphisms in the mu opioid receptor gene (OPRM1) and self-reported responses to
alcohol in American Indians. BMC Medical Genetics,9(1), 35.
McCann, M. J., Miotto, K., Rawson, R. A., Huber, A., Shoptaw, S., & Ling, W (1997).
Outpatient Non‐Opioid Detoxification for Opioid Withdrawal. The American Journal on
Addictions, 6(3), 218-223.
Schuckit, M. A (2009). Alcohol-use disorders. The Lancet, 373(9662), 492-501.
Rodd, Z. A., Bertsch, B. A., Strother, W. N., Le-Niculescu, H., Balaraman, Y., Hayden, E., ...
& Niculescu, A. B (2006). Candidate genes, pathways and mechanisms for alcoholism: an
expanded convergent functional genomics approach. The pharmacogenomics journal, 7(4),
222-256.
Trim, R. S., Schuckit, M. A., & Smith, T. L (2009). The Relationships of the Level of
Response to Alcohol and Additional Characteristics to Alcohol Use Disorders Across
Adulthood: A Discrete‐Time Survival Analysis. Alcoholism: Clinical and Experimental
Research, 33(9), 1562-1570.
Joslyn, G., Brush, G., Robertson, M., Smith, T. L., Kalmijn, J., Schuckit, M., & White, R. L
(2008). Chromosome 15q25. 1 genetic markers associated with level of response to alcohol in
humans. Proceedings of the National Academy of Sciences, 105(51), 20368-20373.
Goenjian, A. K., Bailey, J. N., Walling, D. P., Steinberg, A. M., Schmidt, D., Dandekar, U.,
& Noble, E. P (2012). Association of TPH1, TPH2, and 5HTTLPR with PTSD and
depressive symptoms. Journal of affective disorders,140(3), 244-252.
Di Chiara, G., Bassareo, V., Fenu, S., De Luca, M. A., Spina, L., Cadoni, C., ... & Lecca, D
(2004). Dopamine and drug addiction: the nucleus accumbens shell
connection. Neuropharmacology, 47, 227-241.
Berke, J. D., & Hyman, S. E (2000). Addiction, dopamine, and the molecular mechanisms of
memory. Neuron, 25(3), 515-532.
Bonoiu, A. C., Mahajan, S. D., Ding, H., Roy, I., Yong, K. T., Kumar, R., ... & Prasad, P. N
(2009). Nanotechnology approach for drug addiction therapy: gene silencing using delivery
of gold nanorod-siRNA nanoplex in dopaminergic neurons. Proceedings of the National
Academy of Sciences, 106(14), 5546-5550.
34. 27
McMahon, F. J., Buervenich, S., Charney, D., Lipsky, R., Rush, A. J., Wilson, A. F., ... &
Manji, H (2006). Variation in the gene encoding the serotonin 2A receptor is associated with
outcome of antidepressant treatment. The American Journal of Human Genetics, 78(5), 804-
814.
Hariri, A. R., Mattay, V. S., Tessitore, A., Kolachana, B., Fera, F., Goldman, D., ... &
Weinberger, D. R (2002). Serotonin transporter genetic variation and the response of the
human amygdala. Science, 297(5580), 400-403.
Lachman, H. M., Papolos, D. F., Saito, T., Yu, Y. M., Szumlanski, C. L., & Weinshilboum,
R. M (1996). Human catechol-O-methyltransferase pharmacogenetics: description of a
functional polymorphism and its potential application to neuropsychiatric
disorders. Pharmacogenetics and Genomics,6(3), 243-250.
Johnson, P. M., & Kenny, P. J (2010). Dopamine D2 receptors in addiction-like reward
dysfunction and compulsive eating in obese rats. Nature neuroscience,13(5), 635-641.
Noble, E. P (2000). Addiction and its reward process through polymorphisms of the D< sub>
2</sub> dopamine receptor gene: a review. European Psychiatry,15(2), 79-89.
MARKOU, A., PATERSON, N. E., & SEMENOVA, S (2004). Role of γ‐Aminobutyric Acid
(GABA) and Metabotropic Glutamate Receptors in Nicotine Reinforcement: Potential
Pharmacotherapies for Smoking Cessation. Annals of the New York Academy of
Sciences, 1025(1), 491-503.
Roberto, M., Cruz, M. T., Gilpin, N. W., Sabino, V., Schweitzer, P., Bajo, M., ... & Parsons,
L. H (2010). Corticotropin Releasing Factor–Induced Amygdala Gamma-Aminobutyric Acid
Release Plays a Key Role in Alcohol Dependence.Biological psychiatry, 67(9), 831-839.
Contet, C., Kieffer, B. L., & Befort, K (2004). Mu opioid receptor: a gateway to drug
addiction. Current opinion in neurobiology, 14(3), 370-378.
Centers for Disease Control and Prevention (2013). Deaths and severe adverse events
associated with anesthesia-assisted rapid opioid detoxification-new york city, 2012. MMWR.
Morbidity and mortality weekly report, 62(38), 777.
Gahoi, S., Arya, L., & Anil, R (2013). DPPrimer–A Degenerate PCR Primer Design Tool.
Bioinformation, 9(18), 9373
Mathews, R., Hall, W., & Carter, A (2012). Direct‐to‐consumer genetic testing for addiction
susceptibility: a premature commercialisation of doubtful validity and value. Addiction,
107(12), 2069-2074.
Liu, J., Huang, S., Sun, M., Liu, S., Liu, Y., Wang, W (2012). An improved allele-specific
PCR primer design method for SNP marker analysis and its application. Plant methods, 8(1),
34.
Substance Abuse and Mental Health Service Administration (samhsa) (2012, January).
Managing Chronic Pain in People With or in Recovery from Substance Use Disorders.
Retrieved from http://www.samhsa.gov/
35. 28
Committee on Advancing Pain Research, Care, Institute of Medicine (US). Committee on
Advancing Pain Research, & Institute of Medicine (2011).Relieving pain in America: A
blueprint for transforming prevention, care, education, and research. National Academies
Press
Ho, M. K., Goldman, D., Heinz, A., Kaprio, J., Kreek, M. J., Li, M. D., & Tyndale, R. F
(2010). Breaking barriers in the genomics and pharmacogenetics of drug addiction. Clinical
Pharmacology & Therapeutics, 88(6), 779-791.
Castro, M (2006). Pharmacogenomics in the Clinic: New Questions About
Tamoxifen. Journal of Oncology Practice, 2(2), 100-100.
Crabbe, J. C (2002). Genetic Contributions to Addiction*. Annual review of psychology,
53(1), 435-462.
Liew, M., Pryor, R., Palais, R., Meadows, C., Erali, M., Lyon, E., & Wittwer, C.
GENOTYPING OF SINGLE NUCLEOTIDE POLYMORPHISMS USING LIGHTCYCLER
GREEN-1.
Ahmadian, A., Gharizadeh, B., O’Meara, D., Odeberg, J., & Lundeberg, J (2001).
Genotyping by apyrase-mediated allele-specific extension. Nucleic acids research, 29(24),
e121-e121.
van Ede, A. E., Laan, R. F., Blom, H. J., Huizinga, T. W., Haagsma, C. J., Giesendorf, B. A.,
... & van de Putte, L (2001). The C677T mutation in the methylenetetrahydrofolate reductase
gene: A genetic risk factor for methotrexate‐related elevation of liver enzymes in rheumatoid
arthritis patients.Arthritis & Rheumatism, 44(11), 2525-2530.
Kosek, E., Jensen, K. B., Lonsdorf, T. B., Schalling, M., & Ingvar, M (2009). Genetic
variation in the serotonin transporter gene (5-HTTLPR, rs25531) influences the analgesic
response to the short acting opioid Remifentanil in humans. Mol Pain, 5, 37.
do Prado‐Lima, P. A. S., Chatkin, J. M., Taufer, M., Oliveira, G., Silveira, E., Neto, C. A., ...
& da Cruz, I. B. M (2004). Polymorphism of 5HT2A serotonin receptor gene is implicated in
smoking addiction. American Journal of Medical Genetics Part B: Neuropsychiatric
Genetics, 128(1), 90-93.
Hosak, L., Libiger, J., Cizek, J., Beranek, M., & Cermakova, E (2006). The COMT
Val158Met polymorphism is associated with novelty seeking in Czech methamphetamine
abusers: preliminary results. Neuro endocrinology letters,27(6), 799-802.
Kim, D. J., Park, B. L., Yoon, S., Lee, H. K., Joe, K. H., Cheon, Y. H., ... & Shin, H. D
(2007). 5′ UTR polymorphism of dopamine receptor D1 (DRD1) associated with severity and
temperament of alcoholism. Biochemical and biophysical research communications, 357(4),
1135-1141.
36. 29
Freire, M. T. M., Marques, F. Z., Hutz, M. H., & Bau, C. H (2006). Polymorphisms in the
DBH and DRD2 gene regions and smoking behavior.European archives of psychiatry and
clinical neuroscience, 256(2), 93-97.
Kieling, C., Genro, J. P., Hutz, M. H., & Rohde, L. A (2008). The− 1021 C/T DBH
polymorphism is associated with neuropsychological performance among children and
adolescents with ADHD. American Journal of Medical Genetics Part B: Neuropsychiatric
Genetics, 147(4), 485-490.
Schinka, J. A., Letsch, E. A., & Crawford, F. C (2002). DRD4 and novelty seeking: results of
meta‐analyses. American journal of medical genetics,114(6), 643-648.
Vandenbergh, D. J., Persico, A. M., Hawkins, A. L., Griffin, C. A., Li, X., Jabs, E. W., &
Uhl, G. R (1992). Human dopamine transporter gene (DAT1) maps to chromosome 5p15. 3
and displays a VNTR. Genomics, 14(4), 1104-1106.
Gerra, G., Leonardi, C., Cortese, E., D'Amore, A., Lucchini, A., Strepparola, G., ... &
Donnini, C (2007). Human kappa opioid receptor gene (OPRK1) polymorphism is associated
with opiate addiction. American Journal of Medical Genetics Part B: Neuropsychiatric
Genetics, 144(6), 771-775.
Chong, R. Y., Oswald, L., Yang, X., Uhart, M., Lin, P. I., & Wand, G. S (2005). The mu-
opioid receptor polymorphism A118G predicts cortisol responses to naloxone and
stress. Neuropsychopharmacology, 31(1), 204-211.
Tan, E. C., Lim, E. C., Teo, Y. Y., Lim, Y., Law, H. Y., & Sia, A. T (2009). Ethnicity and
OPRM variant independently predict pain perception and patient-controlled analgesia usage
for post-operative pain. Mol Pain, 5(32), 1-8.
Nelson, E. C., Lynskey, M. T., Heath, A. C., Wray, N., Agrawal, A., Shand, F. L., ... &
Montgomery, G. W (2014). Association of OPRD1 polymorphisms with heroin dependence
in a large case‐control series. Addiction biology, 19(1), 111-121.
Don, R. H., Cox, P. T., Wainwright, B. J., Baker, K., & Mattick, J. S (1991).
'Touchdown'PCR to circumvent spurious priming during gene amplification.Nucleic acids
research, 19(14), 4008.
Allam, K. V., Cheruku, V., Rangu, N., Mandala, S., & Pogaku, R. R. (2011). Correlating
Genetic Polymorphisms With The Interindividual Variability In Drug Response And
Toxicity. Int J Cur Biomed Phar Res, 1(3), 148-156.
39. Agenda
1. Introduction
a. Project Description
2. Methods and material (Process Details)
Primer Design
a. Step 1: Sample Preparation (PCR)
b. Step 2: PCR Cleanup (SAP-Exo)
c. Step 3: Detection Primer Extension (ASPE)
d. Step 4: Incubation, Washing & Reading
4. Experiments & Results
5. Discussion
a. Current Conditions
b. Further work
1. Optimization
2. Alpha trial
3. Results interpretation
40. 1. 43% of all drug related deaths ~ pain relief medication overdose
2. Death from Opioids > 2 X Death from Heroin & Cocaine
3. 116 million people worldwide are struggling with pain
Retrieved from; Institute of Medicine. 2011 & Centers for Disease Control and Prevention
Elderly
People who suffer from cancer
Injured athletes
Women/ obstetrics pain
relief medication
Necessity of developing APM panel
41. Common pain medications
Hydrocodone
Codeine
Oxycodone
Other Opioids
4. Saving $14.5 billion to in the Unites States
Necessity of developing APM panel
Retrieved from; Institute of Medicine. 2011 & Centers for Disease Control and Prevention
42. Genetic Variations considered in APM Test
Dr. Kenneth Blum
Reward Deficiency Syndrome
(RDS)/ Brain Reward Cascade
Single nucleotide polymorphism or (SNP):
A single base change that occurs at a frequency of
>1% in a given population
45. PCR & ASPE Primer design
F Primer
R PrimerPCR
Primers
Asymmetric Primers
Wild Type
Mutant Type
IIIIIIIIIII
IIIIIIIIIII
Anti-Capture Prob Y
Anti-Capture Prob X
46. Workflow Description
Target Amplification (PCR)
Perform PCR on the DNA via
Thermocycler
PCR Cleanup
Perform SAP-EXO PCR
cleanup
Primer detection extension
Perform Primer Extension on
amplicons via
Thermocycler/Infiniti Plus
Step 2Step 1 Step 3
Detection
Incubation, Washing &
Reading
Step 4
47. Experiments
• Exp1: PCR Annealing Temperature Titration
• Exp2: PCR Primer Titration
• Exp3: PCR Annealing Temperature Titration & no SAP-Exo treatment
• Exp4: Single analyte
• Exp5: DMSO,7-deaza-dGTP Treatment & PCR Annealing Temperature (Comparison
of 64.9C and 59.9C)
• Exp6: ASPE Annealing Temperature Titration
• Exp7: Inclusion of redesigned primers for DRD2 and DRD4
• Exp8: PCR Primer Titration for DRD4
• Exp9: 5HT2A and DRD4 Primers Potential Interference
