Clinical trial NCT03818191
Conditions: Alcohol Use Disorder
Sponsor: Mayo Clinic
Start date: 2019-07-15; Completion date: 2023-11-04
Enrollment: 288 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
AUDs are difficult to treat, and relapse rates are high, with an estimated 80% of individuals with AUDs returning to alcohol use after completing addictions treatment. Novel treatment approaches are needed to enhance long term sobriety. The investigator's research team has been investigating the use of acamprosate to prevent relapse to alcohol use. Unfortunately despite being FDA approved and endorsed by the American Psychiatric Association only 10% of patients treated for AUD are prescribed acamprosate or other antidipsotropic medications. The number is higher for patients treated in programs affiliated with Mayo Clinic Addiction Services (approximately 20%) but is way less than expected. The most common reasons behind these low numbers are the understanding that not every patient benefits from the use of specific medication and the lack of biomarkers predictive of response. The purpose of this project is to identify such biomarkers by discovery of genomic and metabolomic markers associated with response to acamprosate treatment.

Detailed description:

Arms: arm 1: All participants will be randomized to receive acamprosate or placebo in a double-blinded placebo-controlled trial.

The most common side effect associated with acamprosate use is diarrhea, which occurs in approximately 16% of patients. Other frequently occurring side effects include asthenia, nausea, pruritus, and flatulence, headache, abdominal pain, flu syndrome, edema, weight gain, and myalgia. arm 2: All participants will be randomized to receive acamprosate or placebo in a double-blinded placebo-controlled trial.
Interventions: intervention 1: Acamprosate intervention 2: Placebo
Intervention descriptions: intervention 1: The research pharmacy contracted for this study will randomize the study participants for all sites with placebo or acamprosate. intervention 2: The research pharmacy contracted for this study will randomize the study participants for all sites with placebo or acamprosate.

Protocol documents:
Prot_SAP_000.pdf: 
 
Study Title: Pharmacogenomics and Pharmacometabolomics of Acamprosate 
Treatment Outcome  
 
Protocol version number and date: Version 14, May 9, 2023 
 
NCT number: NCT03818191 


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IRB Protocol Template  
 
General Study Information 
 
Principal Investigator:  Victor M. Karpyak, MD, PhD 
 
Co-Investigators:  Richard Weinshilboum, MD; Joanna M. Biernacka, PhD; Mark A. Frye, MD; Liewei Wang, 
MD, PhD; Doo-Sup Choi, PhD; Tyler S. Oesterle, MD, MPH; Bhanu Kolla, MD;  Cedric Skillon, MD; (Hazelden 
Betty Ford Foundation Medical Team);; Quyen Ngo, PhD (Hazelden Betty Ford Foundation Butler Center for 
Research Executive Director)      
 
 
 
 
 
 
 
 
Study Title:   Pharmacogenomics and Pharmacometabolomics of Acamprosate Treatment Outcome 
 
Protocol version number and date:  Version 14, May 9, 2023 
 
Research Question and Aims 
 
Introduction 
Alcohol Use Disorders (AUDs) are prevalent in our country.  The economic and health consequences of AUDs call 
for efficient treatment strategies. AUDs are difficult to treat, and relapse rates are high, with an estimated 80% of 
individuals with AUDs returning to alcohol use after completing addictions treatment.  Novel treatment approaches 
are needed to enhance long term sobriety.  Our research team has been investigating the use of acamprosate to 
prevent relapse to alcohol use.  Unfortunately despite being FDA approved and endorsed by the American 
Psychiatric Association only 10% of patients treated for AUD are prescribed acamprosate or other antidipsotropic 
medications. The number is higher for patients treated in programs affiliated with Mayo Clinic Addiction Services 
(approximately 20%) but is way less than expected. The most common reasons behind these low numbers are the 
understanding that not every patient benefits from the use of specific medication and the lack of biomarkers 
predictive of response. The purpose of this project is to identify such biomarkers by discovery of genomic and 
metabolomic markers associated with response to acamprosate treatment. To achieve this goal, we will first search 
for these biomarkers by re-examining findings from several completed AUD treatment studies involving 
approximately 1,500 patients; next, we will perform a new prospective randomized placebo-controlled trial of 3 
month long treatment with acamprosate and search for genetic and metabolomic biomarkers associated with 
sobriety or relapse in participants of this new study. The combined results of these efforts will provide the largest 
available study sample for discovery of pharmacogenomic and pharmacometabolomic markers of treatment 
response in patients with AUD. The discovery of biomarkers of treatment response is expected to improve 
treatment outcomes by allowing for the personalization of treatment selection. 
 
Specific Aim1: To identify genetic and metabolomic biomarkers associated with acamprosate response in 
previously collected samples. 
 
Specific Aim1a:  
This aim will use existing data from three prior studies [i.e., The Combined Pharmaco-therapies and Behavioral 
Interventions conducted in the United States (COMBINE Study)[1], a double-blind, placebo-controlled 
randomized trial conducted in Germany (PREDICT Study)[2], and “A Pharmacogenomic Probe Study of 
Acamprosate: Genes Associated with Response” Mayo Clinic Center for the Individualized Treatment of Alcohol 


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study (CITA); total N=1687], to assess the heritability explained by common single nucleotide polymorphisms 
(SNPs) and the genetic architecture for different measures of alcoholism treatment response. We will use this data 
to estimate the heritability of different outcomes in patients treated with acamprosate, placebo and naltrexone (as 
active drug control) that can be explained by common SNPs. We will also investigate the sources of genetic 
contribution to different treatment outcomes in terms of the distribution of the genetic signal across minor allele 
frequencies and functional annotation categories. These analyses may help determine if sobriety or another 
treatment outcome (e.g. return to heavy drinking) is the most suitable phenotype for subsequent pharmacogenomic 
analyses of acamprosate treatment response. 
 
Specific Aim1b: 
This aim will use clinical outcomes data from a previously recruited CITA cohort of 445 acamprosate-treated 
alcoholic patients and metabolomics data obtained with two metabolomics analytical platforms to identify 
metabolomic markers associated with length of sobriety as well as additional secondary phenotypes identified in 
Aim 1a.  We will then apply a “pharmacometabolomics-informed pharmacogenomics” research strategy in which 
we will perform genome-wide association studies in search for genetic variants associated with metabolites 
associated with acamprosate clinical outcomes. We will pursue all signals, genes and pathways functionally and 
mechanistically in order to investigate the biological mechanisms underlying sobriety length and other phenotypes 
associated with acamprosate treatment response. This is the same approach that we used previously with success to 
study biological mechanisms contributing to effects of selective serotonin reuptake inhibitors in patients with 
major depressive disorder (MDD).   
 
Specific Aim 2: To identify genetic and metabolomic biomarkers associated with acamprosate response in the new 
cohort of 800 alcoholic patients randomized to acamprosate or placebo treatment. 
 
Specific Aim 2a:  
We will conduct a genome-wide association study (GWAS) in the newly enrolled sample of 800 alcoholic patients. 
Using acamprosate and placebo arms in the new study cohort will allow identification of genetic markers 
associated with acamprosate-specific effects impacting treatment outcomes, including sobriety (primary outcome) 
and other outcomes selected based on the results of Aim 1a. We will also conduct a meta-analysis including 
existing data from aim 1, which will be the largest pharmacogenomic study of AUD treatment outcomes to date 
(total N > 2400). 
 
Specific Aim 2b:  
To identify metabolomic markers associated with length of sobriety and other acamprosate related clinical 
outcomes in patients with AUD recruited to the new trial using the same two metabolomic platforms used in Aim 
1b in order to replicate and extend results found in Specific Aim 1b and to identify new candidate metabolites.  We 
will then once again apply a “pharmacometabolomics-informed pharmacogenomics” research strategy to pursue 
the metabolites found to be associated with drug response outcomes during the placebo-controlled study.  Similarly 
to Aim 1b, we will pursue all signals, genes and pathways functionally and mechanistically. 
 
Specific Aim 3: 
To apply a systematic multiple-omics research strategy to identify molecular and genomic signatures for all 
AUD samples mentioned in aims 1 & 2, as well as mechanisms underlying individual variation in response to 
acamprosate. To achieve this goal, we will obtain transcriptomic profiling and proteomic profiling for AUD 
subjects before and after 3 months of acamprosate therapy. We will then integrate the multiple omics datasets—


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i.e., genomics, metabolomics, transcriptomics and proteomics—using machine learning approaches to develop a 
predictive algorithm for acamprosate response in patients with AUD. 
 
Purpose/Background: 
The staggering costs of alcohol use disorders (AUD) call for the development and implementation of evidence-
based treatment strategies [3, 4]. The results of the National Survey on Drug Use and Health  indicate that less than 
10% of those in need consider treatment for alcohol use disorders (Data Spotlight http://oas.samhsa.gov). 
Moreover, less than 20% of treatment participants remain sober one year later with most of relapses happening 
during the first 3 months after treatment. The introduction of antidipsotropics (acamprosate and naltrexone) as well 
as the alcohol deterrent disulfiram has improved abstinence-related outcomes in some, but not all subjects are 
using those medications (Bouza, Angeles et al. 2004, Suh, Pettinati et al. 2006, Rosner, Leucht et al. 2008). 
However, only 10% choose to use medication as part of their treatment. It is believed that limited use of these 
medications is associated with their variable effectiveness, which may be limited to sub-populations of subjects 
with AUD (Heilig and Egli 2006, Mann, Lemenager et al. 2013). Attention has been called to identification of 
predictors of pharmacological treatment outcomes in different subsets of alcoholic patients (Addolorato, Mirijello 
et al. 2013). Yet, attempts to predict efficacy using clinical variables have not been successful (Verheul, Lehert et 
al. 2005). It is believed that pharmacogenomic research will aid the discovery of such predictors (Kranzler and 
Edenberg, 2010; Litten et al., 2010). 
 
Treatment selection for subjects with AUD also needs to be considered in the context of frequent comorbidity with 
other psychiatric disorders and medical conditions. Of those, the most common and clinically significant are 
depression (Petriks, Gonzalez et al. 2002, Conner, Pinquart et al. 2009) and impaired liver function (Warren and 
Murray 2013). Findings favor acamprosate for the treatment of alcoholics with depression (Lejoyeux and Lehert 
2011), and liver problems (Witkiewitz, Saville et al. 2012), which are common among patients with AUD. On the 
contrary, naltrexone and disulfiram carry potential risk for liver toxicity (Krampe and Ehrenreich 2010, Achunine 
and Taylor 2012). Acamprosate is also the most widely used medication for AUD treatment (Mark, Kassed et al. 
2009) and the proper determination of potential responders and non-responders may result in considerable savings 
in treatment costs (Schadlich and Brecht 1998, Annemans, Vanoverbeke et al. 2000, Mason and Crean 2007). 
Therefore, the identification of biomarkers predicting the ability to respond to acamprosate treatment will be a 
major public health benefit in terms of identifying AUD patients likely to achieve a positive outcome and the least 
side-effects (Mason and Heyser 2010, Hyman 2014).  
 
The FDA approved acamprosate, naltrexone and disulfiram as adjuncts to psychosocial treatment of AUD. 
Consistently, the NIAAA website recommends use of those along with behavioral treatment and mutual support 
groups, emphasizing that “there is no one-size-fits-all solution” 
(https://pubs.niaaa.nih.gov/publications/Treatment/treatment.htm#chapter02). Similarly, contemporary American 
Psychiatric Association (APA) Guidelines recommend that naltrexone or acamprosate be offered to patients with 
moderate to severe alcohol use disorder who have a goal of reducing alcohol consumption or achieving abstinence, 
prefer pharmacotherapy or have not responded to nonpharmacological treatments alone and have no 
contraindications to the use of these medications 
(https://psychiatryonline.org/doi/pdf/10.1176/appi.books.9781615371969). 
 
Consistent with the above mentioned guidelines, all patients treated in the Intensive Addictions program affiliated 
with Mayo Clinic Addiction Services are being offered FDA approved medications for AUD treatment. The 
physician discusses the benefits and risks of adding antidipsotropic medications in the context of individualized 


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assessment of patient’s treatment needs.  In our experience only 15-20% of patients choose to add acamprosate to 
their treatment.   
 
Clearly, response predictors are needed to improve treatment and it is expected that pharmacogenomic research 
will aid in the discovery of such predictors [7, 8]. Moreover, the discovery of biological mechanisms of response to 
acamprosate as a “probe drug,” may guide development of innovative treatment approaches and personalized 
recommendations for the treatment of patients with AUD. 
 
We previously demonstrated an association of the glutamate ionotropic receptor N-methyl-D-aspartate (NMDA) 
type subunit 2B (GRIN2B) rs2058878 variant with sobriety length in two independent samples of acamprosate-
treated alcoholics [9]. Yet, these findings were based on a candidate gene/pathway approach and potentially missed 
other important variations, suggesting a need for an unbiased and comprehensive search for genetic markers of 
response. Moreover, although clinical data favors sobriety as a treatment outcome associated with acamprosate, no 
studies have explored whether sobriety is more heritable than other alcoholism treatment outcomes, and thus it is 
unknown whether sobriety is the most appropriate outcome for pharmacogenomics investigations. Furthermore, 
the intermediate phenotypes, which are more proximal to genetic variation compared to behavioral phenotypes, are 
known to improve the power of genetic association studies [10, 11]. We have previously demonstrated that change 
in the plasma metabolite levels is a powerful intermediate phenotype allowing for the identification of genes 
associated with treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants – an approach 
called ‘pharmacometabolomics-informed pharmacogenomics’ [12]. However, this powerful approach has not been 
used in pharmacogenomic studies of antidipsotropic medications. Yet, preliminary metabolomic analyses of our 
data indicated that acamprosate responders had elevated serum glutamate at baseline, which decreased during 
treatment [13]. Similarly, our brain imaging studies showed an association of glutamate levels in the left 
dorsolateral prefrontal cortex with alcohol cravings and response to acamprosate treatment [14, 15]. 
 
Thus, our preliminary findings indicate that genetic variation as well as metabolite (glutamate) levels in human 
plasma and brain tissue may be associated with acamprosate response. Yet, these findings, based on a candidate 
gene/pathway approach, potentially ignored other important variations and the lack of placebo arm in the original 
study precluded the differentiation between acamprosate-specific associations from those related to other factors 
contributing to sobriety. Moreover, we have only investigated genetic associations with sobriety and it is unknown 
if stronger association may exist with other treatment outcomes. Therefore, the goal of this study is to identify 
genetic markers associated with acamprosate response by using highly innovative strategies including: (1) 
polygenic analysis of existing data from genome-wide studies of acamprosate response to determine appropriate 
phenotype for pharmacogenetic study and (2) search for genetic markers associated with acamprosate vs. placebo 
treatment response in AUD patients on a genome-wide scale in the combined sample including alcoholics treated 
by acamprosate and placebo in the COMBINE, PREDICT and CITA studies and a new sample of 800 AUD 
patients treated in community-based programs in a double blind randomized placebo controlled study of 
acamprosate. This will allow us to perform a meta-analyses of genome-wide meta-analyses of AUD treatment 
outcomes in the largest combined sample used for pharmacogenomic studies in the field of alcoholism research 
(total N>2400). This project involves an innovative, integrated and comprehensive series of studies of the 
pharmacometabolomics and “pharmacometabolomics-informed pharmacogenomics” of acamprosate in the 
treatment of patients suffering from AUD.  The experiments proposed subsequently have been designed  to 
identify metabolomic biomarkers for response to acamprosate therapy and then to subject those metabolomic 
biomarkers to genome-wide association studies (GWAS) to identify SNPs and genes associated with 
concentrations of the metabolites associated with drug response phenotypes—SNPs and genes which can then be 


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pursued functionally for their biological plausibility and to gain insight into novel mechanisms underlying the 
observed associations.   
 
Study Design and Methods 
 
Research Design  
Specific Aim1: To identify genetic and metabolomic biomarkers associated with acamprosate response in 
previously collected samples. 
 
Specific Aim1a:  
Statistical analyses will be applied to assess the heritability explained by common single nucleotide 
polymorphisms (SNPs) and the genetic architecture of different measures of outcomes of treatment with 
acamprosate, placebo and naltrexone (as active drug control). We will also investigate the sources of genetic 
contribution to different treatment outcomes in terms of the distribution of the genetic signal across minor allele 
frequencies and functional annotation categories using advanced statistical analysis methods as described under 
analysis plan. These analyses may help determine if sobriety or another treatment outcome (e.g. return to heavy 
drinking) is the most suitable phenotype for subsequent pharmacogenomic analyses of acamprosate response. 
 
Specific Aim1b: 
We will apply a “pharmacometabolomics-informed pharmacogenomics” research strategy in which we will 
identify metabolomic markers associated with length of sobriety as well as additional secondary phenotypes 
followed by performing genome-wide association studies for the metabolites associated with acamprosate clinical 
outcomes.   We will then pursue all signals, genes and pathways functionally and mechanistically. 
 
Specific Aim 2: To identify genetic and metabolomic biomarkers associated with acamprosate response in the new 
cohort of 800 alcoholic patients randomized to acamprosate or placebo treatment. 
 
Specific Aim 2a:  
We will conduct a double-blind randomized placebo-controlled study of treatment response to acamprosate in the 
newly enrolled sample of 800 patients with AUDs participating in the community based treatment programs and 
use clinical outcomes of this study for genome-wide association  analyses searching for genetic markers associated 
with treatment outcomes. 
 
Specific Aim 2b:  
To identify metabolomic markers associated with length of sobriety and other acamprosate related clinical 
outcomes in 800 patients with AUD recruited to a new trial in which the patients will be randomized to 
acamprosate or placebo using the same two metabolomic platforms used in Aim 1b in order to replicate and extend 
results found during the earlier non-placebo controlled study of 445 AUD patients and to identify new candidate 
metabolites.  We will then apply a “pharmacometabolomics-informed pharmacogenomics” research strategy to 
pursue the metabolites found to be associated with drug response outcomes during the placebo-controlled study.   
 
Using acamprosate and placebo arms in the new study cohort will allow identification of genetic markers 
associated with acamprosate-specific effects impacting treatment outcomes, including sobriety (primary outcome) 
and other outcomes selected based on the results of Aim 1. We will also conduct a meta-analysis of 


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pharmacogenomic effects on acamprosate response, which will be the largest pharmacogenomic study of AUD 
treatment outcomes to date (total N > 2000). 
 
To accomplish Aims 2a  and 2b, 800 patients meeting Diagnostic and Statistical Manual of Mental Disorders, Fifth 
Edition (DSM5) criteria for AUD will be recruited from community-based treatment programs affiliated with 
Mayo Clinic and Mayo Health System and Hazelden Betty Ford Foundation and enrolled in a double-blind 
randomized placebo-controlled trial of acamprosate (Fig. 1).  
 
Specific aim 3: 
To accomplish Aim 3, we will be collecting and storing additional blood samples for multiple-omics methods to 
identify genetic and other markers associated with acamprosate treatment outcomes..  For example, state-of-the-art 
induced pluripotent stem cells (iPSC) technology has undoubtedly become a powerful research tool which holds 
great potential in neuropsychiatric research, including addiction research.  The iPSCs offer a unique opportunity to 
create cell types (both normal and pathological phenotypes) for target tissues from a variety of organs, for example 
but not limited to, the central nervous system, the brain, and “organoids”.  Due to the high cost and the lengthy 
process required for iPSC generation, we will generate a panels of iPSCs for functional genomics studies.  These 
genomic studies for all AUD samples mentioned in aims 1 & 2, when combined with metabolomic, proteomic, 
transcriptomic, and other molecular studies, and clinical outcomes will provide an opportunity to apply multiple 
“omic” approaches to obtain novel insight into the underlying pathophysiology of AUD, which will serve as a 
unique scientific resource. 
 
Recruitment process and infrastructure 
Recruitment sites: Patients will be recruited from study sites affiliated with the Mayo Clinic Addiction Services in 
the Rochester and Albert Lea campuses (Intensive Addiction treatment Program, Fountain Center Residential 
Treatment Program in Albert Lea, MN,  Department of Psychiatry & Psychology Mayo Clinic Albert Lea) and 
Hazelden Betty Ford Foundation treatment Centers in Center City, MN and Plymouth, MN. Hazelden Betty Ford 
Foundation St. Paul location will be an alternative site to conduct study visits (no recruitment activities) to improve 
options for patient traveling while participating.  With the integration of Rochester and the Health System sites into 
Mayo Midwest Department of Psychiatry & Psychology we have the ability to recruit at these campuses in the 
context of the integration and standardization of electronic medical records, nomenclature, clinical practices, and 
optimization of data capture, management and reporting. In addition, arrangements were made with the leadership 
of Hazelden Betty Ford Foundation allowing ensuring efficient enrollment process and data collection at the 
affiliated study sites. A brief description of each treatment program is presented below.  Recruitment sites of Mayo 
Health System Albert Lea and Hazelden Betty Ford Foundation Treatment Centers (Center City & Plymouth) will 
rely on Mayo Clinic IRB and will be added when agreements are in place. 
 
1. The Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, MN has two treatment 
programs for persons 18 and older, which integrate a combination of psychosocial treatments with evidence-
based pharmacotherapies of addiction under the direction of Board Certified addiction psychiatrists. The 
Intensive Addiction Program (IAP) is a 30-day residential treatment program that provides treatment 9 hours 
per day, 6.5 days per week, and the Outpatient Addiction Program (OAP) that provides an outpatient treatment 
program limited to 4 hours a day, 5 days per week. The combined annual census of the IAP and the OAP is 267 
patients. 
   


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2. The Fountain Center Residential Treatment Program in Albert Lea, MN is a counselor-lead residential program 
for persons 18 and older with an emphasis on teaching relapse prevention skills.  Fountain Center integrates the 
12 Steps of Alcoholics Anonymous into the program.  Annual census is 201 patients. Mayo has recently 
provided a shuttle service between Rochester and Albert Lea. 
 
3. The Hazelden Betty Ford treatment facility in Center City, MN is a 140 bed residential, and outpatient 
treatment facility which serves persons 18 and older with Alcohol and Substance Use Disorders. The goal of 
the program is to ensure each client’s health returns to daily living through intensive, comprehensive treatment.  
Annual census is ~1500 patients including ~80% with AUD. 
 
4. The Hazelden Betty Ford treatment facility in Plymouth, MN is a residential and outpatient treatment facility 
with specialized programming for adolescents, teenagers, and young adults ages 12-25 with alcohol and 
substance use disorders. Annual census is ~ 815 patients. 
 
5. The Hazelden Betty Ford treatment facility in Newburg, OR is a residential and outpatient treatment facility 
which serves persons 18 and older with alcohol and substance use disorders. Annual census is ~ 850 patients. 
 
Study Assessments and Procedures 
Assessments 
A standardized clinical assessment is performed for all patients admitted to above mentioned treatment programs. 
This assessment will be used by study personnel to determine study eligibility, and determine if there is a need to 
discuss treatment of depression and/or anxiety. The Study Psychiatrist affiliated with each study site, or a member 
of the clinical treating team will meet with the patient to assess appropriateness and discuss potential benefits and 
risks associated. For patients meeting study inclusion and exclusion criteria and signing Informed Consent, the 
presence of AUD and comorbid conditions including depressive and anxiety disorders will be confirmed by 
Psychiatric Research Interview for Substance and Mental disorders (PRISM).  The severity of depression and 
anxiety symptoms will be assessed by the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety 
Disorder (GAD-7), respectively. In addition, alcohol use history, alcohol craving intensity, LFT levels and other 
relevant measures will be assessed at baseline and repeated at follow up visits as defined in Table 1.  
 
(1) We will standardize depression and anxiety treatment by accepting current and stable use of SSRI & SNRI 
medications listed in table 3 (page 14) in all participants with depression and/or anxiety diagnoses. This 
will allow for an assessment of the impact of depression and/or anxiety as well as their standardized 
treatment on acamprosate and placebo-related treatment outcomes and account for this impact in genetic 
and metabolomic association analyses. Patients with newly diagnosed anxiety or depression as well as 
patients with known diagnosis but not taking any antidepressant will be offered treatment with escitalopram 
or citalopram as first line treatment during study participation when their GAD-7 or PHQ-9 scores are 
equal or above 10. We selected escitalopram based on evidence suggesting it is strongest among selective 
serotonin reuptake inhibitors (SSRIs) [18] and favoring it over other antidepressants in terms of efficacy 
and tolerability [19]. Clinical findings in adults with AUD and depression as well as our experimental data 
also suggest that the combined use of escitalopram and acamprosate may improve treatment outcomes [20, 
21].  This threshold has been chosen based on previous research showing that PHQ-9 score ≥ 10, indicative 
of the presence of at least moderate depression and/or a GAD-7 score of ≥ 10, indicative of the presence of 
at least moderate anxiety reflect a clinically significant level of symptom severity warranting the use of 
medications [16, 17]. Results of treatment with escitalopram or citalopram will be monitored by the study 


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psychiatrist affiliated with each study site who will make appropriate treatment recommendations as 
described below.  
 
(2) To avoid the impact of end stage liver disease on clinical and metabolomic findings, we will use a Model 
for End-Stage Liver Disease (MELD) score of 10 and below as a cut off for inclusion in the study, and will 
exclude patients with a diagnosis of advanced liver disease or those with a MELD score above 10, which is 
considered an indication that the extent of liver damage has reached an advanced stage [22, 23]. The 
MELD score will be calculated according to the standard approach using serum concentrations of bilirubin 
and creatinine, and the international normalized ratio for prothrombin time [22].  
 
Study flow and Schedule of Events 
Study personnel will explain the research risks and benefits. Written informed consent will be obtained for each 
study subject. The baseline diagnostic assessment, triage, treatment group assignment and follow up assessments 
are summarized in Fig. 1 and described in more detail in Specific Aim 2. Information regarding severity of alcohol 
withdrawal symptoms (measured by CIWA scale or site specific equivalent method) and medications used during 
withdrawal treatment preceding enrollment in the study will be collected or abstracted from the medical record and 
included in the data analyses. 
 
*Notation dated 5-12-2023: Upon IRB approval, subjects will no longer be randomized to the placebo arm.  
Instead, for the remainder of the study, all patients will be provided acamprosate to achieve the active drug and 
placebo ratio of 2:1.   
Figure 1:  Study Flow Chart 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The presence of AUD and other psychiatric disorders will be confirmed by a computer version of the Psychiatric 
Research Interview for Substance and Mental Disorders (PRISM) [24]. The presence of comorbid depressive 
disorders requiring treatment will be determined by the combination of 2 characteristics: (1) the presence of any 
* 


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diagnosis of depression during the previous year as confirmed by PRISM and verified by the study psychiatrist; 
and (2) a PHQ-9 score of 10 or above and/or a GAD-7 score of 10 or above. The interview will be conducted by 
interviewers trained and certified on the use of the PRISM. Interviewers, who have submitted required recordings 
and documents for certification and an unexpected delay in the certification process occurs, will be allowed to 
conduct prism assessment, if no other certified staff are available, with certification pending. The research 
pharmacy contracted for this study will randomize the study participants for all sites with placebo or acamprosate. 
 
The schedule of study procedures and assessments is tabulated by visit in the Study Assessments in Table 1 
(below).  A visit window of +/- 10 days is allowed when scheduling.  Questionnaires may be distributed in 
multiple formats (paper, electronic/emailed link, or asked/read to patient.  
 
 


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Table 1:  Study Assessments 
Study 
Assessments 
Baseline 
Evaluation 
Follow-Up Visits 
2 week – 
TC 
1 month 
♯ 
6 week –  
TC 
2 month 
♯ 
10 week – 
TC 
3 month 
♯ 
Blood Collection 
X 
 
X 
 
 
 
X 
Medication provided  X 
 
X 
 
X 
 
 
PRISM 
X 
 
 
 
 
 
 
LTDH 
X 
 
 
 
 
 
 
CIWA-Ar 
X 
 
 
 
 
 
 
TLFB ♦ (BDQ) 
X 
 
X 
 
X 
 
X 
PHQ-9 
X 
X 
X 
X 
X 
X 
X 
GAD7 
X 
X 
X 
X 
X 
X 
X 
AAM 
 
 
X 
 
X 
 
X 
IDTS 
X 
 
 
 
 
 
 
PACS 
X 
 
X 
 
 
 
X 
PRISE 
X 
 
X 
 
X 
 
X 
FIBSER 
 
 
X 
 
X 
 
X 
PSQI 
X 
 
X 
 
X 
 
X 
FIRM 
X 
 
 
 
 
 
 
CD-RISC 
X 
 
 
 
 
 
X 
Med. Compliance  
 
 
X 
 
X 
 
X 
Med. Compliance 
Enhancement 
 
X 
 
X 
 
X 
 
CTA 
X 
 
 
 
 
 
 
GGT lab test 
X 
 
X 
 
 
 
X 
CTQ 
X 
 
 
 
 
 
 
CGI 
 
 
X** 
 
X** 
 
 
Definitions: AAM: AA attendance monitoring. CIWA-R: Clinical Institute Withdrawal Assessment-Research. CTA: Commitment to 
Abstinence. CTQ: Childhood Trauma Questionnaire. FIBSER: Frequency, Intensity and Burden of Side Effects Ratings. GGT: 
gamma-glutamyl transpeptidase lab test. IDTS: Inventory of Drug Taking Situations. LTDH: Lifetime Drinking History. PACS: Penn 
Alcohol Craving Scale. PHQ-9: Patient Health Questionnaire 9-item. GAD7: Generalized Anxiety Disorder 7-item.  PRISE: Patient-
rated Inventory of Side Effects. PRISM: Psychiatric Research Interview for Substance and Mental Disorders. TC: Telephone Call 
TLFB: The Alcohol Timeline Follow Back. PSQI: Pittsburgh Sleep Quality Index.  FIRM: Family Index of Risk for Mood.  CD-RISC: 
Connor-Davidson Resilience Scale (CD-RISC); CGI: Clinical Global Impression (CGI); **Used by study psychiatrist to assess 
worsening mood symptoms despite antidepressant treatment during study participation.  ♦ Brief Drinking Questionnaire (BDQ).   
♯ Allow the use of alternative formats to face-to-face visits based on concern for COVID exposure or other circumstances 
potentially preventing patients from attending face-to-face visits (blood draw done in person). 
 
 
Description of Assessment Instruments: 
PRISM:  Psychiatric Research Interview for Substance and Mental Disorders (PRISM) will be used for 
standardized assessment of Substance Use Disorders and comorbid conditions. PRISM is an electronic computer-
based tool providing systematic coverage of the longitudinal course of alcohol- and drug-related experiences and 
psychiatric symptoms that may be useful in identifying areas of focus for treatment. The PRISM follows a 
decision-tree format using diagnostic algorithms for all Axis 1 psychiatric disorders plus Borderline and Antisocial 
Personality Disorders. Acceptable reliability and diagnostic validity of PRISM has been demonstrated [25, 26]. 
 


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LTDH:  The Lifetime Drinking History questionnaire (LTDH) provides a self-report of patterns of alcohol 
consumption over a person’s lifetime [27]. A modified version allowing for the assessment of initial tolerance and 
maximum alcohol consumption in a 24-hour period will be used to provide the clinical projects with a description 
of the study samples and to investigate the potential impact of the LTDH on the primary and secondary treatment 
outcomes. 
 
CIWA-Ar:  The revised Clinical Institute Withdrawal Assessment for Alcohol (CIWA-Ar) scale.  This assessment 
for monitoring withdrawal symptoms requires approximately 5 minutes to administer.  The maximum score is 67.  
Patients scoring less than 10 do not usually need additional medication for withdrawal [28]. 
 
TLFB:  The Alcohol Timeline Follow Back (TLFB) is a drinking assessment method that obtains estimates of 
daily drinking and has been evaluated with clinical and nonclinical populations. Using a calendar, people provide 
retrospective estimates of their daily drinking over a specified time period that can vary up to 12 months from the 
interview date[29]. 
 
PHQ-9:  The 9-Item Patient Health Questionnaire (PHQ-9). The PHQ-9 consists of nine questions, rated 0–3 
according to the increased frequency of difficulty experienced in each area covered. Scores, with a possible range 
of 0–27, are summed and can then be interpreted as follows: no depression (0), minimal (1–5), mild (6–9), 
moderate (10–14), moderately severe (15–19), or severe (>20) depression. 
 
GAD-7:  Generalized Anxiety Disorder 7 (GAD-7) is a self-reported questionnaire for screening and severity 
measuring of generalized anxiety disorder (GAD). GAD-7 has seven items, which measure severity of various 
signs of GAD according to reported response categories with assigned points. This will take approximately 5 
minutes to complete. 
 
AA Monitoring:  Self-monitoring of attendance of Alcoholics Anonymous meetings (AAM) and sponsorship [30] 
will be used as a potential covariate and predictor of treatment response.  
Evidence supports the importance of motivation for sobriety and attendance of support groups for treatment 
outcomes in alcoholics [31, 32]. Therefore, we will collect CTA and AAM data to allow for the assessment of the 
impact of these factors on treatment outcomes. 
 
IDTS:  The Inventory of Drug-Taking Situations (IDTS), developed by Annis and Martin (1985) [33], is a 50-item 
self-report questionnaire that provides a profile of the situations in which a client has used alcohol or another drug 
over the past year. Clients are asked to indicate their frequency of heavy drinking or drug use in each of 50 
situations on a 4-point scale ranging from “never” to “almost always.” The questionnaire may be administered in 
either pencil-and-paper or computerized version; the latter allows a client to name up to three substances that are 
currently causing a problem; the 50 IDTS items are presented for each substance in turn, and a computer-generated 
report is produced for each substance. 
 
PACS:  The Penn Alcohol Craving Scale (PACS) is a five-item self-administered instrument for assessing 
frequency, intensity, and duration of craving [34, 35]. The questions on the PACS use descriptors coupled with 
numerical ratings ranging from 0 to 6. PACS will be used to assess intensity of craving at baseline and follow up 
visits as a potential predictor of treatment response.  
 


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Intensity of craving and its contextual meaning (e.g. negative or relief craving) were associated with abstinence 
from alcohol and acamprosate response [36-38]. Therefore, we will collect PACS and IDTS data to allow for 
assessment of the impact of these factors on treatment outcomes. 
 
PRISE:  Patient-Rated Inventory of Side Effects (PRISE) is a self-report form used to qualify adverse effects as 
tolerable or distressing in 9 different domains, each with multiple symptoms [39]. 
 
FIBSER:  Frequency, Intensity and Burden of Side Effects Ratings (FIBSER) is a self-report form used to quantify 
the adverse effect burden [40]. 
  
CTA:  Commitment to Abstinence (CTA) will be determined by the subject’s response to a questionnaire 
developed by Hall and colleagues in 1990 and modified with permission [41]. 
 
CTQ:  The Childhood Trauma Questionnaire (CTQ) was developed as screening tools for histories of abut and 
neglect. The self-report includes a 28-item test that measures 5 types of maltreatment – emotional, physical, and 
sexual abuse, and emotional and physical neglect. Approximately 5 minutes is required to complete the test. A 5-
point Likert scale is used for the responses which range from Never True to Very Often True. 
 
FIRM:  Family Index of Risk for Mood (FIRM) is a self-reported tool developed to gather family history of 
psychiatric and seizure illness.  The FIRM has demonstrated clinical significance in detecting pediatric bipolar 
disorder. [44] 
 
CD-RISC: Connor-Davidson Resilience Scale (CD-RISC) is a self-rating scale to assess resilience.  Resilience 
may be viewed as a measure of stress coping ability and, as such, could be an important target of treatment in 
anxiety, depression, and stress reactions.  The CD‐RISC has sound psychometric properties and distinguishes 
between those with greater and lesser resilience. [45] 
 
CGI: Clinical Global Impression (CGI) is an assessment used by study psychiatrist for clinical judgment of 
depression symptoms’ severity and worsening symptoms during the course of study participation. [46] 
 
Side Effects Assessment:  Assessment of side effects is critical to ensure safety of study participants and for the 
assessment of tolerability of the treatments. Therefore, we will collect PRISE and FIBSER data to monitor study 
participants for safety during treatment with study medications and to allow for the assessment of the impact of 
these factors on treatment outcomes. 
 
PSQI: Pittsburgh Sleep Quality Index is a self-rated questionnaire which assesses sleep quality and disturbances 
over a 1-month time interval.  Nineteen individual items generate seven “component” scores including: subjective 
sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping 
medication, and daytime dysfunction. 
 
BDQ: Brief Drinking Questionnaire is used as an alternative to collecting drinking information if a patient refuses 
to complete the TLFB assessment.  Six questions are asked to assess relapsed drinking during a period of time (i.e. 
between follow-up visits). 
 
Remuneration: 


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All participants will be paid $40 each for completing Baseline, Month 1 and Month 2 visits and paid $50 for 
completing the Month 3 visit for a total of $170.  Non-Mayo patients will be given a Visa gift card for the 
appropriate dollar amount corresponding to completed visits.  Mileage reimbursement will be available for travel 
costs up to the amount of $75 per visit. 
 
Study Medications:  
All participants will be randomized to receive acamprosate or placebo* in a double-blinded placebo-controlled 
trial. Acamprosate, SSRIs, and SNRIs listed  are FDA approved medications for the indications for which they are 
being used in this protocol; therefore the risk to participants is no more than that to the general population who 
may be taking these medications.  Medication/Placebo* administration starts with 1 pill 3 times a day for 7 days 
then increases to 2 pills 3 times a day.  
 
*Notation dated 5-12-2023: Upon IRB approval, subjects will no longer be randomized to the placebo arm.  
Instead, for the remainder of the study, all patients will be provided acamprosate to achieve the active drug and 
placebo ratio of 2:1. 
 
Potential Side Effects: Participants could be at risk of the potential side effects of one or both of these 
medications, which they will be made aware of at the time of obtaining informed consent and will be monitored on 
a monthly basis. Acamprosate is reasonably well tolerated in most patients. The most common side effect 
associated with acamprosate use is diarrhea, which occurs in approximately 16% of patients. Other frequently 
occurring side effects include asthenia, nausea, pruritus, and flatulence, headache, abdominal pain, flu syndrome, 
edema, weight gain, and myalgia. Infrequent side effects include ascites, face edema, photosensitivity reaction, 
suicidal ideation, suicide attempts, completed suicide, and acute kidney failure. The potential serious side effects 
of acamprosate are rare and include cardiomyopathy, deep vein thrombophlebitis, heart failure, and mesenteric 
arterial occlusion. The common side effects of SSRIs, and SNRIs listed on table 3 (page 14) are, but not limited 
to, diaphoresis, abdominal pain, constipation, diarrhea, indigestion, nausea, vomiting, xerostomia, dizziness, 
headache, insomnia, somnolence, disorder of ejaculation, impotence, orgasm incapacity, reduced libido, and 
fatigue. The potential serious side effects of are worsening depression, suicidal thoughts, and serotonin syndrome. 
It is possible that the intensity of depressive or anxiety symptoms may increase despite treatment and patients may 
develop suicidal ideation. It is also possible that patients may become medically unstable due to conditions 
unrelated to study treatment. The study team will meet regularly to review any side effects or adverse events 
reported and modify the protocol accordingly. Study participants developing side effects requiring medical 
attention will be referred to appropriate medical facilities.  
 
In-study risks and safety procedures:   
General considerations: Participant’s treatment response as well as frequency and intensity of adverse effects will 
be discussed at baseline and assessed during follow-up visits with study staff.  The study blind will be removed if 
any Unanticipated Problems Involving Risk To Subjects or Others (UPIRTSO) or adverse events is determined to 
be potentially related to the study medication (as deemed by treating study psychiatrist and study PI). Should 
concerns emerge regarding the patient’s clinical problems not related to this study, those should be addressed by 
primary care specialist of patient’s choice If deemed necessary by study personnel, an urgent discussion with a 
study psychiatrist will take place for evaluation, and appropriate referral for treatment will be made. Study 
consent form includes participant’s consent for permission to interface with his/her primary provider should 
clinical concerns arise.  
  


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Depression Safety Management Plan:  The study team members will not be providing clinical care for conditions 
other than treatment of AUD and comorbid depression or anxiety. Therefore, any findings from PRISM assessment 
raising safety concerns, including positive response to suicide questions will trigger immediate notification to 
study psychiatrist and patient’s clinician. In addition, if a subject endorses suicidality with any of the following: 
Active suicidal ideation as determined by the investigator or as determined by PHQ-9 (Question 9 score of 1 or 
higher) or if a subject responds positively to questions regarding the harm of self and/or others, the research team 
will immediately contact the study psychiatrist to refer for appropriate treatment.  
 
During the follow up visits, questionnaires that involve questions related to depression not involving immediate 
concerns of harm to self or others will be reviewed within 1 working day of receipt from the subject. Changes 
reflecting worsening of depressive and anxiety symptom severity scores as collected using PHQ-9 and GAD-7, 
respectively, will be reported to the study psychiatrist who will determine if further assessment is necessary to 
ensure appropriate medical care and the safety of the study participant. The Clinical Global Impression (CGI) tool 
will be used to reflect assessment of those symptom changes by study psychiatrist. Study participants whose 
condition deteriorates to the point of being judged unstable (defined as a score of >6 on CGI) by a study 
psychiatrist, will be withdrawn from the study and referred to appropriate treatment. If emergent concerns arise 
regarding risk of harm to self or others, the patient will be referred for emergency care; if declined, authorities will 
be contacted for a well person check.  Study PI or covering MD will be available to discuss questions related to 
management of depression, anxiety or other study-related aspects of patient care. 
 
Resources:   
We will utilize substantial resources available at Mayo Clinic, such as the Clinical Research Unit (CRU), to 
support recruitment and proper specimen collection and procurement at the Mayo Health System sites. The 
addition of the large clinical operation at the Hazelden Betty Ford Foundation treatment facilities in Center City 
and Plymouth, MN will significantly increase the referral base allowing for a more rapid achievement of the 
recruitment targets; and trained personnel (in all research sites) enabling large-scale prescreening, selection, 
enrollment and monitoring of the study participants will be required to achieve this goal.  Adding Hazelden St. 
Paul location will improve visit options for patient travel while participating in the study. 
  
Genome-wide genotyping will be conducted by the Mayo Clinic ‘Medical Genome Facility’, which offers a variety 
of genotyping assays, including state-of-the-art genome-wide SNP arrays. 
 
The amino acid metabolomic platform assays using the CITA samples will be performed at the Mayo Clinic 
Metabolomic Core Facility. During year 2, metabolomic assays for neuromodulator metabolites will be performed.  
The acamprosate placebo-controlled trial recruitment will continue throughout years 1, 2 and 3—with completion 
of the trial at the end of year 3. 
 
The Statistics Team, which will be responsible for data management, biostatistics and bioinformatics support.  The 
statistical team (Dr. Biernacka, Postdoc, MS Statistician, and Statistical Programmer) will meet weekly to plan and 
review ongoing data analyses. Dr. Joanna Biernacka, the Statistics Team Director and Co-PI for this study, 
obtained a PhD degree in biostatistics, with a focus on statistical genetics, followed by further training in statistical 
genetics as a postdoctoral research fellow. She joined Mayo Clinic’s Program in Genomics of Addiction ten years 
ago.   
 


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  (1a)  This is a multisite study involving Mayo Clinic and non Mayo Clinic sites. When checked, describe in 
detail the research procedures or activities that will be conducted by Mayo Clinic study staff. 
 
  (1b)  Mayo Clinic study staff will be engaged in research activity at a non Mayo Clinic site.  When checked, 
provide a detailed description of the activity that will be conducted by Mayo Clinic study staff. 
 
Subject Information 
 
Target accrual:  800 (400 Mayo Clinic; 400 Hazelden Addiction Treatment Centers) 
 
Subject population:  Adults (male and female) 
 
Inclusion Criteria:   
1. Age 18 to85; DSM-5 (14) diagnosis of AUD determined by PRISM;  
2. Completion of alcohol detoxification (CIWA score < 5) and no alcohol for at least 7 days (but no more than 
35 days);  
3. Ability to provide informed consent 
4. Ability to speak English  
5. Willingness to use the study medications for 3 months and attend follow-up visits. 
6. No chronic/daily use of benzodiazepines, opioids, or stimulants for a period of time which is determined by 
3 x the medication half-life value (see addendum A) to be completed before the initiation of study 
medication (acamprosate or placebo).   
7. Willingness to discontinue previously prescribed acamprosate for a period of at least 3 days before 
randomization to study medication (acamprosate or placebo*) which allows for metabolomic signature 
without medication. 
 
*Notation dated 5-12-2023: Upon IRB approval, subjects will no longer be randomized to the placebo arm.  
Instead, for the remainder of the study, all patients will be provided acamprosate to achieve the active drug and 
placebo ratio of 2:1. 
 
Exclusion Criteria:   
1. Hypersensitivity or allergy to acamprosate  
2. Current use of wellbutrin and not willing to switch to an acceptable antidepressant medication  
3. Renal impairment (creatinine level >1.5 mg/dL);  
4. Diagnosis of advanced liver disease indicated in the medical record or by a MELD score of above 10;  
5. Women who are pregnant, breastfeeding, or planning to become pregnant during the next year;  
6. Primary diagnosis of substance use disorder other than alcohol as determined by PRISM or in medical 
record review or secondary diagnosis of active (within the past year) benzo/sedative dependence,  opioid 
dependence,  stimulant dependence, heroin dependence, and/or cocaine dependence 
7. Refusal to abstain from any chronic/daily use of prescribed benzodiazepines, opioids, stimulants, cannabis 
related medication such as CBD or medical marijuana, during the course of participation. 
8. Current use of Naltrexone and not willing to stop and switch to Acamprosate/Placebo*(see notation dated 
5-12-2023 above) 
9. Current use of Antabuse. 


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10. Active suicidal ideation or any unstable medical or psychiatric condition as determined by responses to 
PRISM or by the investigator. 
11. Status of involuntary or court-ordered admission at time of consent. 
 
Research Activity 
 
Check all that apply and complete the appropriate sections as instructed.   
1. 
  Drug & Device:  Drugs for which an investigational new drug application is not required. Device for 
which (i) an investigational device exemption application is not required; or the medical device is 
cleared/approved for marketing and being used in accordance with its cleared/approved labeling. (Specify in 
the Methods section) 
 
2. 
  Blood:  Collection of blood samples by finger stick, heel stick, ear stick, or venipuncture.  
 
3. 
  Biological specimens other than blood:  Prospective collection of human biological specimens by 
noninvasive means that may include: urine, sweat, saliva, buccal scraping, oral/anal/vaginal swab, sputum, hair 
and nail clippings, etc. 
 
4. 
  Tests & Procedures:  Collection of data through noninvasive tests and procedures routinely employed in 
clinical practice that may include: MRI, surface EEG, echo, ultrasound, moderate exercise, muscular strength 
& flexibility testing, biometrics, cognition testing, eye exam, etc.  (Specify in the Methods section) 
 
5. 
  Data (medical record, images, or specimens):  Research involving use of existing and/or prospectively 
collected data. 
 
6. 
  Digital Record:  Collection of electronic data from voice, video, digital, or image recording. (Specify in 
the Methods section) 
 
7. 
  Survey, Interview, Focus Group:  Research on individual or group characteristics or behavior, survey, 
interview, oral history, focus group, program evaluation, etc.  (Specify in the Methods section) 
 
 NIH has issued a Certificate of Confidentiality (COC).  When checked, provide the institution and investigator 
named on the COC and explain why one was requested. ________________________ 


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Biospecimens – Categories 2 and 3 
 
(2)  Collection of blood samples. When multiple groups are involved copy and paste the appropriate section below 
for example repeat section b when drawing blood from children and adults with cancer.  
 
a. From healthy, non-pregnant, adult subjects who weigh at least 110 pounds. For a minimal risk 
application, the amount of blood drawn from these subjects may not exceed 550ml in an 8 week period and 
collection may not occur more frequently than 2 times per week. 
Hazelden Betty Ford Treatment facilities in Plymouth, MN and Newburg, OR, Mayo Clinic Health 
System site in Albert Lea, MN patient blood volume per blood draw:  35ml  
 
Other location’s (Mayo Clinic Rochester & Hazelden Betty Ford, Center City, MN) patients’ blood 
volume per blood draw:  50ml at Baseline (preferred but can be done at any visit) and 35 ml at Months 
1 & 3  
Hazelden Center City will offer participants to provide the additional ~15ml of blood as optional 
(preferred at baseline visit but can be done at any visit) for generation of the induced pluripotent stem 
cells (iPSCs) from peripheral blood mononuclear cells (PBMC) as described below.   
 
Frequency of blood draw: three time points, Baseline, 1 and 3 month visits.  
 
Blood samples for 35ml collection are for GGT levels: Serum Separator Tube (SST); DNA extraction, 
Acamprosate blood levels, Metabolomic, Proteomic biomarkers (EDTA tubes), and mRNA (PAXGene 
tube).  The process of spinning and aliquoting the samples will be completed at each site. EDTA tubes 
that would need to be kept on ice, centrifuged in the cold (NOT frozen) with the plasma aliquoted into 
200 ul aliquots which would need to be immediately placed and stored at minus 80 degrees.  The 
current metabolomic platform would include both the amines and neuromodulators.  Acamprosate 
blood drug levels will be plasma aliquoted into 300 ul aliquots and also assayed with these samples.  
PAXGene tube is placed directly into the minus 80 degree freezer. 
 
An additional 15 ml of blood collected in EDTA tubes for peripheral blood mononuclear cells (PBMC) 
isolation. The separation and banking of PBMCs will be performed in the laboratory, frozen and stored 
in liquid nitrogen.  PBMC are used to generate induced pluripotent stem cells (iPSCs), a cell type that 
can self-renew and which can be to differentiated into many cell types from many human tissues.  
 
 
Review of medical records, images, specimens – Category 5 
 
For review of existing data: provide a date range or an end date for when the data was generated. The end date 
can be the date this application was submitted to the IRB.  Example: 01/01/1999 to 12/31/2015 or all records 
through mm/dd/yyyy.  


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Date Range:  12/29/2004 to 07/09/2013 
Check all that apply (data includes medical records, images, specimens).  
 
  (5a)  Only data that exists before the IRB submission date will be collected.   
 
 
  (5b)  The study involves data that exist at the time of IRB submission and data that will be generated after IRB 
submission. Include this activity in the Methods section.  
 
  (5c)  The study will use data that have been collected under another IRB protocol.  
 
 Data    
 Specimens  
 Data & Specimens IRB #16-004978 “Pharmacogenomics of Treatment Outcomes in 
Alcohol Use Disorders”  
 
 Data    
 Specimens  
 Data & Specimens _IRB #07-007204 “A Pharmacogenomic Probe Study of 
Acamprosate: Genes Associated with Response” Mayo Clinic Center for the Individualized Treatment of Alcohol 
Dependence” (CITA) Recruitment dates: 03/14/2008 to 02/01/2013  
 
 Data    
 Specimens  
 Data & Specimens _IRB #2681-04 “Developing a DNA Repository for Genomic 
Studies of Addiction” (GOA) Recruitment dates: 12/29/2004 to 07/09/2013 
 
  (5d)  This study will obtain data generated from other sources. Examples may include receiving data from 
participating sites or an external collaborator, accessing an external database or registry, etc.  Explain the source 
and how the data will be used in the Methods section. 
  
  (6)  Video audio recording: Describe the plan to maintain subject privacy and data confidentiality, 
transcription, store or destroy, etc.  
 
 
HIPAA Identifiers and Protected Health Information (PHI) 
 
Protected health information is medical data that can be linked to the subject directly or through a combination of 
indirect identifiers.  
 
Recording identifiers (including a code) during the conduct of the study allows you to return to the medical record 
or data source to delete duplicate subjects, check a missing or questionable entry, add new data points, etc. De-
identified data is medical information that has been stripped of all HIPAA identifiers so that it cannot be linked 
back to the subject. De-identified data is rarely used in the conduct of a research study involving a chart review.   
 
Review the list of subject identifiers below and, if applicable, check the box next to each HIPAA identifier 
being recorded at the time of data collection or abstraction.  Identifiers apply to any subject enrolled in the 
study including Mayo Clinic staff, patients and their relatives and household members.  
 
Internal refers to the subject’s identifier that will be recorded at Mayo Clinic by the study staff. 
External refers to the subject’s identifier that will be shared outside of Mayo Clinic. 
 


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Check all that apply: 
INTERNAL 
EXTERNAL 
Name 
X 
 
Mayo Clinic medical record or patient registration number, lab accession, 
specimen or radiologic image number  
X 
 
Subject ID, subject code or any other person-specific unique identifying 
number, characteristic or code that can link the subject to their medical data   
X 
 
Dates: All elements of dates [month, day, and year] directly related to an 
individual, their birth date, date of death, date of diagnosis, etc.   
Note: Recording a year only is not a unique identifier.  
X 
 
Social Security number 
 
 
Medical device identifiers and serial numbers 
 
 
Biometric identifiers, including finger and voice prints, full face photographic 
images and any comparable images 
 
 
Web Universal Resource Locators (URLs), Internet Protocol (IP) address 
numbers, email address 
 
 
Street address, city, county, precinct, zip code, and their equivalent geocodes 
 
 
Phone or fax numbers 
 
 
Account, member, certificate or professional license numbers, health 
beneficiary numbers 
 
 
Vehicle identifiers and serial numbers, including license plate numbers 
 
 
Check ‘None’ when none of the identifiers listed above will be recorded, 
maintained, or shared during the conduct of this study.  (exempt category 4) 
 None 
 None 
 
 
Data Analysis 
 
Statistical Analyses and Power  
Analysis for Specific Aim 1a 
The data that will be used in Aim 1a has undergone quality control (QC) as a part of a prior project (IRB #16-
004978).  For the analysis of heritability captured by common SNPs, we will first estimate the heritability of 
different measures of AUD treatment response (e.g. length of sobriety, return to heavy drinking or percent days 
abstinent) explained by common SNPs using the mixed linear model approach implemented in the GCTA software 
(104).  This approach requires more stringent QC than for standard GWAS. Thus, we will begin by performing 
additional QC in each dataset, which will include: the exclusion of one of each pair of individuals who share more 
than 2.5% of their genetic material demonstrating distant relatedness, and exclusion of SNPs with Hardy-Weinberg 
Equilibrium (HWE) p-value <10-3. Because these methods can produce biased results in the presence of population 
stratification, these analyses will be performed in an ancestrally homogenous subsample, specifically participants 
of European Ancestry as they represent the vast majority of the full sample (Table 2). Principal Component 
Analysis (PCA) will be used to identify outliers for exclusion, ensuring an ancestrally homogeneous subset of 
participants. The analysis of imputed data will also be performed, as described below; for these analyses only 
SNPs imputed with very high confidence (R2>0.9) will be retained. Following QC, we will use GCTA to calculate 
the Genetic Relationship Matrix (GRM) and will then use a linear mixed model with residual maximum likelihood 
(REML) analysis to estimate the variance attributable to the SNPs (i.e. SNP-based heritability) for each treatment 


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outcome measure. These analyses will include study, and principal components that capture ancestry as covariates. 
Estimates (and confidence intervals) for the SNP heritability of different outcomes will be compared. 
 
Analysis for Specific Aim 1b 
Metabolite concentrations will be log-2 transformed, and associations between baseline metabolite concentrations 
and the time of abstinence since baseline will be evaluated by linear regression with age and gender as covariates.  
If Timeline Follow Back (TLFB) data at 3-months is absent, the date of last contact will serve as the day of first 
lapse unless the subject has reported a lapse at an earlier time point. To study the possible effect of depression 
severity and liver function, regression models including baseline PHQ-9 scores and LFTs (AST, ALT, and GGT) 
will be compared with those without to determine whether the metabolites remain significantly correlated with 
treatment outcome. Participants who provided paired blood samples will be classified as responders (maintenance 
of complete abstinence) or non-responders (any consumption of alcoholic beverages during the trial period), and 
their metabolite concentrations at baseline and at 3 months will be compared by two-way ANOVA to detect any 
significant changes between the two time points and whether these changes differ significantly between groups.  
 
After metabolomic markers associated with length of sobriety (or additional secondary phenotypes) are identified, 
we will apply a “pharmacometabolomics-informed pharmacogenomics” research strategy. Specifically, we will 
perform genome-wide association analyses to search for genetic variants associated with metabolites that are 
associated with acamprosate clinical outcomes. We will then evaluate the association of the identified genetic 
variants (p<5*10-8) with clinical treatment outcomes in all available datasets (including the COMBINE and 
PREDICT datasets used in Aim 1a). We will also pursue all signals, genes and pathways functionally and 
mechanistically in order to investigate the biological mechanisms underlying sobriety length and other phenotypes 
associated with acamprosate treatment response. This is the same approach that we used previously with success to 
study biological mechanisms contributing to effects of selective serotonin reuptake inhibitors in patients with 
major depressive disorder (MDD).   
 
Analysis for Specific Aim 2a 
Genetic association analyses will evaluate the effect of SNP genotypes on acamprosate and placebo treatment 
outcomes. Well-established pipelines will be used for genotype quality control and imputation prior to performing 
the genetic association analyses. We will first perform genome-wide association analyses of the primary outcomes 
in the acamprosate-treated participants with available 3 month outcomes, using standard approaches. For example, 
we will evaluate the association of SNPs with 3 month continuous abstinence (yes/no) using logistic regression and 
with length of abstinence using Cox proportional hazard models. We will first use these models to evaluate the 
association of the treatment outcomes with covariates, including demographic and baseline clinical characteristics, 
such as depression and anxiety symptom severity (PHQ-9 and GAD-7 scores, respectively), and liver function tests 
(LFTs) – aspartate aminotransferase (AST), alanine transaminase (ALT) and Gamma-Glutamyltransferase (GGT). 
SNP association with the primary treatment outcomes will be evaluated while adjusting for relevant covariates and 
principal components derived from genome-wide SNP data to adjust for population stratification. As discussed in 
the Statistics Team Section, secondary analyses will also be performed using the entire sample of participants 
enrolled into the study [“intent-to-treat” (ITT) analysis]. If the same genetic variants contribute to lack of response 
in the “completer” group and to early dropout (which is likely the case as early dropout frequently results from 
relapse), the ITT analysis will have greater power than the “completer” analysis. We will also perform genome-
wide association analyses using all participants (acamprosate and placebo), including a treatment covariate, and a 
SNP-treatment interaction to evaluate whether SNPs are associated with clinical outcomes in a drug-dependent 
manner (e.g. SNP is associated with acamprosate treatment outcome, but not placebo treatment outcome). We will 


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also evaluate the possibility of sex-specific genetic effects, or genetic effects that are modified by the presence of 
comorbidities such as depression and anxiety, by exploratory analyses involving interaction terms with these 
variables. 
 
Most importantly, we will perform a genome-wide meta-analysis of acamprosate pharmacogenomics using the 
new data and data from available prior studies (COMBINE, PREDICT, CITA; see Aim 1).  


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Table 2 Subject Characteristics 
COMBINE 
PREDICT 
CITA 
N 
1393 
426 
443 
Age, mean(SD) 
44.4 (10.2) 
45.3 (8.7) 
42.1 (11.8) 
Sex, N(%) male 
955 (69%) 
328 (77%) 
286 (65%) 
Race, N(%) white non-Hispanic 
1062 (77%) 
426 (100%) 
412 (93%) 
Baseline Consumption Measures (last 30 days)1 
 
 
 
 Average drinks per drinking day 
13.0 (8.1) 
19.4 (11.3) 
11.8 (7.7) 
 % drinking days 
72.8 (23.5) 
82.1(1.3) 
50.7 (29.7) 
 % heavy drinking days 
65.2 (24.9) 
79.0 (1.3) 
45.8 (29.7) 
 Number of days since last drink 
7.7 (5.3) 
22.1 (4.4) 
24.8 (16.1) 
Medications2 
 Acamprosate, N 
 Naltrexone, N 
 No active drug (placebo or no medication), N 
 
608 
614 
466 
 
172 
169 
85 
 
443 
0 
0 
Treatment outcomes3 
 
 
 
 3 month abstinence from any drinking, N(%) 
293 (21.2) 
194 (45.5) 
132 (29.7) 
 3 month abstinence from heavy drinking, N(%) 
432 (31.2) 
210 (49.3) 
168 (37.9) 
1For COMBINE and CITA, baseline consumption is based on 30 days prior to the start of medication, while for 
PREDICT it is based on 30 days prior to the onset of inpatient treatment, as proposed by Mann and colleagues (84). 
2For the COMBINE study, the number of participants for each medication may include participants on more than one 
medication (i.e. groups are not mutually exclusive since the study included acamprosate/naltrexone co-treatment). 
3Numbers for treatment outcomes are based on the ITT sample (i.e. complete sample) assuming those who dropped 
out prior to 3 months had returned to heavy drinking. 
 
As with the analysis described above, our meta-analyses will also include analyses of the full set of acamprosate 
and placebo treated participants, with a treatment covariate and tests of SNP-treatment interaction effects. 
However, our power estimate for the meta-analysis is based only on the combined sample of acamprosate-treated 
patients, since the size of this sample will determine the power to detect acamprosate-specific pharmacogenomics 
effects. Assuming a total sample of 1000 acamprosate-treated participants (ITT), the meta-analysis will provide 
80% power to detect associations of SNPs with 3-month abstinence during acamprosate treatment (assume ~1/3 
abstain and 2/3 relapse) with odds ratios of 2.07 and 1.80 for SNPs with minor allele frequencies (MAFs) of 0.2 
and 0.4, respectively, at a genome-wide significance level of 5*10-8. We also note that analyses of the full dataset 
(N≈1500) including participants that received acamprosate or other treatment options will provide greater power to 
detect genetic predictors of treatment-independent predictors of abstinence in patients with AUD. 
 
We recognize that the available sample has relatively little power to detect the effects of individual SNPs, which 
tend to be very modest for complex traits such as alcohol-dependence related phenotypes. We will therefore apply 
more advanced analysis methods to maximize power of the study, including gene-level and pathway-based (gene-
set) analyses using approaches such as MAGMA [42], and will evaluate polygenic risk score associations and 
AUD treatment outcomes. 
 
 Analysis for Specific Aim 2b: 
 
Analysis for Aim 2b will use a similar strategy the analysis described above in aim 1b. However, in Aim 2b 
analyses will be performed on the much larger new sample and will also include assessment of drug interactions to 
evaluate whether metabolomics predictors of treatment outcome differ between acamprosate-treated and placebo-


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treated patients. After identifying metabolites that predict treatment outcomes, we will again apply a 
pharmacometabolomics-informed pharmacogenomics analysis strategy, but genetic associations will be evaluated 
in the full sample of patients with genetic and treatment outcomes data, including the existing samples used in Aim 
1 and the new samples collected in Aim 2. 
 
Analysis for Specific Aim 3 will be completed in the future utilizing the latest methodology and analyses.  
  
Limitations and Proposed solutions   
• The proposed study design may not allow for the separation of the effects of depression and anxiety from 
the treatment effects of SSRIs, and SNRIs listed on an association between genetic variation and 
acamprosate or placebo-related treatment outcomes. Separation of those effects would have required an 
additional placebo arm for antidepressant treatment. We considered this option but concluded it would not 
be feasible and may negatively affect the attainment of the primary study goal - i.e. identification of genetic 
markers associated with acamprosate-related treatment outcomes in participants with AUD. Therefore, the 
difference between the effects related to presence/severity of depression or anxiety and the effects of 
antidepressant treatment on an association between genetic variation and acamprosate or placebo-related 
treatment outcomes will need to be investigated in future studies. 
• Although the size of the new study sample proposed for the pharmacogenomic analyses in Aim 2 is 
comparable or exceeds the size of samples collected for the CITA, COMBINE and PREDICTS studies, this 
sample alone is still unlikely to provide sufficient power to detect significant associations. However, as 
described above, our goal is to conduct a meta-analysis to identify pharmacogenomic markers associated 
with response to acamprosate in a combined sample including participants enrolled as part of the proposed 
center as well as those collected in the COMBINE, PREDICT and CITA studies. This will be the largest 
pharmacogenomics GWAS in AUD to date. Moreover, the sample will also include a large set of placebo-
treated participants (N≈800) allowing for differentiation between acamprosate-specific pharmacogenomic 
effects from treatment independent predictors of abstinence in patients with AUDs. 
 
We also expect that the application of pharmacometabolomics- and imaging-guided pharmacogenetic analyses of 
the acamprosate-related outcomes described in Aim 3 will allow for the use of intermediate phenotypes (namely 
metabolomic and neuro-imaging signatures associated with acamprosate response) and will improve the power for 
the discovery of genetic variations predictive of response to acamprosate. 
 
Endpoints: 
The primary treatment outcome will be defined as continuous sobriety (yes/no) during 3 months of treatment. 
Secondary outcomes (also assessed during 3 months of treatment) will include:  
(1) The number of days until first alcohol use assessed by TLFB. 
(2) Number of days until first relapse (≥ 5 drinks/day for men and ≥ 4 drinks/day for women) will be 
assessed using TLFB.  
(3) Cumulative abstinence duration proportion: the proportion of days over the length of follow-up during 
which participants were completely abstinent from alcohol use, a score range of 0 (drinking 
continuously) to 100 (maintain complete abstinence) is applied. 
 


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Addendum A (Medication Half-life)  
 
Abbreviation Key 
  
SL:  Sublingual 
SR:  Sustained Release 
IM:  Mucous Membrane 
DR:  Delayed Release 
IR:  Immediate Release 
ER:  Extended Release 
DS:  Double Strength 
SD:  Sustained Delivery 
ODT:  Orally Disintegrating Tablets 
PM:  Evening Formulation 
LA:  Long Acting 
IV:  Intravenous  
 
Benzodiazepine Generic Drug 
Name 
Trade Drug Name(s) 
Half-Life 
Min/Max 
Half-Life Breakdown 
Alprazolam 
Xanax, Xanax XR, 
Gabazolamine-0.5,  
Niravam 
10.7-19.7 hours 
XR: 10.7-15.8 hrs; Disintegrating: 
12.5 hrs; IR: 11.2 hrs; W/alcoholic 
liver disease: 19.7 hrs;  
Elderly Population: 16.3 hrs 
Chlordiazepoxide 
(Hydrochloride) 
Librium 
24-48 hours 
N/A 
Chlordiazepoxide Hydrochloride/ 
Clidinium Bromide 
Librax 
5-30 hours 
N/A 
Chlordiazepoxide/ 
Amitriptyline Hydrochloride 
Limbitrol, Limbitrol DS 
5-93 hours 
Systemic: Chlordiazepoxide 5-30 
hrs; Amitriptyline: 8-93 hrs 
Clobazam 
Onfi, Sympazan 
36-82 hours 
Clobazam: 36-42 hours;  
N-desmethylclobazam: 71-82 hrs 
Clonazepam 
KlonoPIN, KlonoPIN 
Wafers 
30-40 hours 
N/A 
Clorazepate (Dipotassium) 
Gen-xene, Tranxene T-Tab, 
Tranxene-SD, Tranxene 
2 days 
clorazepate dipotassium: 2.29 hrs; 
Nordiazepam: 2 days 
Diazepam 
Diastat, Diazepam 
Intensol, Valium 
Up to 100 hours 
Compound: Up to 48 hours 
(alcoholic liver cirrhosis can 
prolong 2-5 fold); 
desmethyldiazepam: Up to 100 hrs 
Estazolam 
Prosom 
10-34.6 hours 
10-24 hours; 59-68 year olds: 
13.5-34.6 hours (18.4 mean) 
Flurazepam  
(Discontinued in 2019) 
N/A 
2.3-100 hours 
Compound: 2.3 hs; N-1-
desalkylflurazepam: 47-100 hrs; N-
1-hydroxyethylflurazepam: 16 hrs 
Flurazepam Hydrochloride 
(Discontinued in 2019) 
Dalmane 
2.3-100 hours 
Compound: 2.3 hours; N-1-
desalkylflurazepam: 47-100 hours 
Halazepam 
Paxipam 
14-100 hours 
14 day use 34.7 hrs;  
N-desmethyldiazepam: 50-100 hrs 
(14 day use of halazepam, half-life 
for the metabolite was 57.9 hours) 
Lorazepam 
Ativan, Lorazepam Intensol 12-14 hours 
N/A 


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Midazolam, nasal 
Nayzilam 
2.1-7.2 hours 
Compound: 2.1-6.2 hours; 
Metabolite: 2.7-7.2 hours 
Midazolam Hydrochloride 
Versed, Seizalam 
1.8-6.4 hours 
N/A 
Nitrazepam 
N/A 
24-29 hours 
N/A 
Oxazepam 
Serax 
5.7-10.9 hours 
N/A 
Prazepam 
N/A 
30-200 hours 
Mean half-life of 78 hours 
Quazepam 
Doral 
39 hours 
N/N 
Temazepam 
Restoril 
3.5-18.4 hours 
Mean half-life of 8.8 hours 
Triazolam 
Halcion 
1.5-5.5 hours 
N/A 
 
Stimulant Generic Drug 
Name 
Trade Drug Name(s) 
Half-Life 
Min/Max 
Half-Life Breakdown 
Amphetamine 
Dyanavel XR, Adzenys 
XR ODT, Adzenys XR 
11-15.12 hours 
D-amphetamine: 11-12.36 hours; 
L-amphetamine: 14-15.12 hours 
Amphetamine Sulfate 
Evekeo, Evekeo ODT 
7-34 hours 
N/A 
Benzphetamine Hydrochloride 
Didrex 
6-12 hours 
N/A 
Dextroamphetamine 
N/A 
12 hours 
N/A 
Dextroamphetamine Sulfate 
Dexedrine, Dextrostat, 
Liquadd, ProCentra, 
Zenzedi 
12 hours 
N/A 
Dextroamphetamine/ 
Amphetamine 
Adderall, Adderall XR, 
Mydayis 
10-13 hours 
N/A 
Lisdexamfetamine Dimesylate 
Vyvanse 
< 1 hr-12 hours 
Compound: < 1 hr; Metabolite 
(dextroamphetamine): Half-life  
about 12 hrs after giving dose  
Methamphetamine 
Hydrochloride (oral) 
Desoxyn 
4-5 hours 
N/A 
Armodafinil 
Nuvigil 
15 hours 
N/A 
Dexmethylphenidate 
Hydrochloride 
Focal, Focalin XR 
2.2 hrs-3 hrs 
XR: 3 hours; IR: 2.2 hours 
Diethylpropion Hydrochloride 
Tenuate, Tenuate Dospan 4-8 hours 
N/A 
Mazindol 
N/A 
30 hrs-5.25 days 
Parent Compound: 30-50 hrs; 
Metabolites: 5.25 days 
Methylphenidate Hydrochloride 
Concerta, Jornay PM, 
Jornay ER, Jorany DR, 
Metadate ER, Methylin, 
Methylin ER, Ritalin, 
Ritalin LA, Ritalin-SR 
2.5-7 hours 
Depends on trade name & release: 
IR: 2.7-3.5 hrs; Jornay DR/ER: 5.9 
hrs, Adhansia ER: 7 hrs; Metadate 
and Ritalin ER: 2.5-6.8 hrs; 
Concerta ER: 3.5 hrs 
Modafinil 
Provigil 
15 hours 
N/A 
Pemoline 
Cylert (no longer available) 
11-13 hours 
N/A 
Phendimetrazin Tartrate 
Bontril, Bontril PDM, 
Bontril slow-release, 
Melfiat, Obezine, Phendiet, 
Phendiet-105, Prelu-2 
2-4 hours 
Applies to both IR and SR 


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Phenmetrazine 
N/A 
Within 24 hrs 
Eliminated in urine within 24 
hours after dosing 
Phentermine Hydrochloride 
Adipex, Atti-plex P, 
Fastin, Phentercot, 
Phentride, Pro-Fast, 
Adipex-P, Lomaira 
7-8 hours 
N/A 
Phentermine Resin 
Lonamin 
7-8 hours 
N/A 
Phentermine/Topiramate 
Qsymia 
20-65 hours 
Phentermine: 20 hours; 
Topiramate: 65 hours 
Cocaine 
N/A 
1 hour 
N/A 
 
Opioid Generic Drug Name 
Trade Drug 
Name(s) 
Half-Life 
Min/Max 
Half-Life Breakdown 
Acetaminophen/Caffeine/ 
Dihydrocodeine Bitartrate 
Panlor-DC,  
Panlor-SS,  
Zerlor, Trezix 
2-5 hours 
Acetaminophen: 2-3 hours; Caffeine:  
5-6 hours; Dihydrocodeine Bitartrate: 
3.5-5 hours 
Acetaminophen/Codeine 
Phosphate 
APAP w/ codeine, 
Capital w/ codeine, 
Pyregesic-C, Vopac, 
Tylenol w/ codeine, 
Tylenol w/ codeine #3, 
Tylenol w/ codeine #4 
1-4 hours 
Acetaminophen: 1-4 hours;  
Codeine Phosphate: 2.5-3 hours 
Acetaminophen/Oxycodone 
Hydrochloride 
Endocet, Percocet, 
Roxicet, Roxilox, 
Tylox, Narvox, 
Magnacet, Perloxx 
3.9-6.9 hours 
Acetaminophen: ER: 5.8-6.9 hrs, IR: 
4.1 hrs; Oxycodone Hydrochloride: 
ER: 4.5-5.4 hrs, IR: 3.9 hrs 
Alfentanil Hydrochloride 
Alfenta 
90-111 minutes 
N/A 
Aspirin/Caffeine/ 
Dihydrocodeine Bitartrate 
Synalgos DC 
3.5-5 hours 
Aspirin: 15 mins; Caffeine: 5-6 hrs; 
Dihydrocodeine Bitartrate: 3.5-5 hrs 
Aspirin/Codeine Phosphate 
Empirin w/ codeine 
2.9-3 hours 
Codeine Phosphate: 2.9 hours; 
Salicylic acid: 3 hours 
Aspirin/Oxycodone 
Hydrochloride 
N/A 
5.6 hours 
Aspirin: 15 minutes; Salicylate: 2-3 
hours; Oxycodone: 5.6 hours 
Belladonna/Opium 
N/A 
3-24 hours 
Belladonna: Up to 24 hrs;  
Opium: 3-10 hrs 
Benzohydrocodone/ 
Acetaminophen 
Apadaz 
4.33-4.78 hours 
Hydrocodone: 4.33 hours; 
Acetaminophen: 4.78 hours 
Buprenorphine 
Butrans, Belbuca, 
Probuphine, 
Sublocade 
11 hours-60 
days 
Buccal film, subdermal implant, 
transdermal patch: 24-48 hours;  
SubQ ER Injections: 43-60 days; 
Hypoalbuminemia: 11 hours 
Buprenorphine Hydrochloride 
Buprenex, Subutrex 
1.2-35 hours 
IV: 1.2-7.2 hours; SL: 31-35 hours 


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Buprenorphine/Naloxone 
Bunavail, Suboxone, 
Zubsolv, Cassipa 
1.9-42 hours 
Buccal film: buprenorphine 16.4-27.5 
hrs; naloxone 1.9-2.4 hrs; Sublingual 
film: buprenorphine 24-48 hrs; 
naloxone 2-12 hrs 
Butalbital/Acetaminophen/ 
Caffeine/Codeine Phosphate 
Phrenilin w/ caffeine 
and codeine, Fioricet 
w/ codeine 
1.25-35 hours 
Butalbital: 35 hours; Acetaminophen: 
1.25-3 hours; Caffeine: 3 hours; 
Codeine Phosphate: 2.9 hours 
Butalbital/Aspirin/Caffeine/ 
Codeine Phosphate 
Ascomp w/ codeine, 
Fiorinal w/ codeine 
2.9-35 hours 
Butalbital: 35 hrs; Aspirin:12 mins; 
Caffeine: 3 hrs; Codeine Phosphate: 
2.9 hrs 
Butorphanol 
N/A 
4-7 hours 
N/A 
Butorphanol Tartrate 
Stadol, Stadol NS 
4.56-5.8 hours 
N/A 
Carisoprodol/Aspirin/Codeine 
Phosphate 
N/A 
2-9.6 hours 
Compound: carisoprodol 2 hrs; aspirin 
15 mins; codeine phosphate 2.9 hrs; 
Meprobamate: 9.6 hrs; Salicylic acid: 
6 hrs 
Chlorpheniramine 
Polistirex/Codeine Polistirex 
Tuzistra XR 
5-21.45 hours 
Chlorpheniramine Polistirex: 21.45 hrs; 
Codeine Phosphate: 5 hours 
Codeine Sulfate 
N/A 
3 hours 
N/A 
Codeine Phosphate/ 
Guaifenesin 
Allfen CD, Allfen 
CDX, Tussiden C, 
Tusso-C, Virtussin 
A/C, Dex-Tuss, 
ExeClear-C, 
Guaifenesin AC, 
Guaiatussin AC, 
Guiatuss AC, Mar-Cof 
CG Expextorant, 
Robitussin AC 
N/A 
N/A 
Dezocine 
N/A 
2.6-2.8 hours 
N/A 
Difenoxin Hydrochloride/ 
Atropine Sulfate 
Motofen 
24-72 hours 
N/A 
Diphenoxylate Hydrochloride/ 
Atropine Sulfate 
N/A 
2.5-4.5 hours 
Diphenoxylate Hydrochloride: 2.5 hrs; 
Atropine Sulfate: 2.5 hours; 
Diphenoxylic Acid: 4.5 hours 
Fentanyl 
Duragesic, Subsys, 
Ionsys 
3-27 hours 
IV: 3-12 hrs; Sublingual: 5.25-11.99 
hrs; Transdermal patch: 20-27 hrs 
Fentanyl Citrate 
Actiq, Sublimaze, 
Fentora, Onsolis, 
Abstral, Lazanda 
2.63-24.9 hours 
Intranasal spray: 15-24.9 hrs; IM/IV: 
219 mins; Oral (troche/lozenges): 7 hrs; 
Oral (bucal tabs): 2.63-4.43 hrs(100-
200 mcg), 11.09-11.7 hrs (400-800 
mcg); Oral bucal soluble films: 14-19 
hrs; Sublingual tab: 5.02-6.67 hrs (100-
200 mcg), 10.1-13.5 hrs (400-800 mcg) 
Fentanyl/Droperidol 
N/A 
2-4 hours 
Fentanyl: 2-4 hrs; Droperidol: 2.2 hrs 


Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
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Hydrocodone Bitartrate 
N/A 
7-9 hours 
Tablets: 7-9 hours; Capsules: 8 hours 
Hydrocodone Bitartrate/ 
Chlorpheniramine Maleate 
Vituz 
4-24 hours 
Hydrocodone: 4 hours; 
Chlorpheniramine: 21-24 hours 
Hydrocodone Bitartrate/ 
Acetaminophen 
Lorcet, Lortab, Vicodin 
HP, Anexsia, 
Maxidone, Norco, 
Zydone, Ceta Plus 
3.8 hours 
Hydrocodone: 3.8 hours; 
Acetaminophen: 1.25-3 hours 
Hydrocodone Bitartrate/ 
Guaifenesin 
FluTuss XP, ExeCof XP, 
Extendryl HC, Hydro-
Tussin HG, Narcof, 
ExeClear, Canges-XP,  
Monte-GHC 
4-5 hours 
Hydrocodone: 4-5 hrs;  
Quaifenesin: 1 hour 
Hydrocodone Bitartrate/ 
Homatropine Methylbromide 
Hycodan, Hydromet, 
Tussigon 
4 hours 
Hydrocodone: 4 hours; Homatropine 
Methylbromide: Unknown 
Hydrocodone Bitartrate/ 
Ibuprofen 
Reprexain, 
Vicoprofen, Ibudone 
4.5 hours 
Hydrocodone: 4.5 hrs;  
Ibuprofen: 2.2 hrs 
Hydrocodone Bitartrate/ 
Pseudoephedrine Hydrochloride Pancorf HC, Rezira 
4-6 hours 
Hydrocodone: 4 hrs; Pseudoephedrine 
Hydrochloride: 4-6 hrs 
Hydrocodone Polistirex/ 
Chlorpheniramine Polistirex 
TussiCaps, Tussionex 
Pennkinetic 
4-24 hours 
Hydrocodone: 4 hours; 
Chlorpheniramine: 16-24 hours 
Hydrocodone/Chlorpheniramine/
Pseudoephedrine 
Notuss-Forte, Hyphed, 
Hydron PSC, Zutripro 4-24 hours 
Hydrocodone: 4 hours; 
Chlorpheniramine: 21-24 hours; 
Pseudoephedrine: 4-6 hours 
Hydrocodone/Pseudoephedrine/ 
Guaifenesin 
Hycofenix 
4-6 hours 
Hydrocodone: 4 hours; 
Pseudoephedrine: 4-6 hours; 
Guaifenesin: 1 hour 
Hydromorphone Hydrochloride 
Dilaudid, Dilaudid-5, 
Dilaudid-HP, Palladone, 
Exalgo 
2.3 hours 
Intravenous 
Hydromorphone 
N/A 
11 hours 
ER Tablets 
Levomethadyl 
N/A 
35-60 hours 
N/A 
Levorphanol 
N/A 
11 hours 
N/A 
Levorphanol Tartrate 
Levo-Dromoran 
11-16 hours 
N/A 
Meperidine Hydrochloride 
Demerol, Meperitab 
3-48 hours 
Compound: 3-8 hours; Metabolite 
(normeperidine): 20.6-48 hours 
Meperidine Hydrochloride/ 
Promethazine Hydrochloride 
Mepergan 
N/A 
N/A 
Methadone Hydrochloride 
Dolophine, Methadone 
HCL Intensol, 
Methadose, Methadose 
Diskets, Methadose 
Dispersible 
8-59 hours 
N/A 
Morphine Sulfate 
AVINza, Kadian, MS 
Contin, Morphabond 
ER, Oramorph SR, 
Roxanol-T, Rms 
2-15 hours 
IV: 2 hours; Kadian XR: 11-13 hours; 
Kapanol™ SR: 15 hours 


Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
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Morphine Sulfate Liposome 
DepoDur 
2-32.9 hours 
(+/- 24.2 hours) 
Epidural: 4.2 hrs (+/- 2.1 hrs)-32.9 hrs 
(+/- 24.2 hours); Intravenous: 2 hours 
Morphine Sulfate/Naltrexone 
Hydrochloride 
Embeda 
29 hours 
Morphine Sulfate: 29 hours; 
Naltrexone Hydrochloride: No results 
Nalbuphine 
N/A 
2.2-5 hours 
N/A 
Nalbuphine Hydrochloride 
Nubain 
5 hours 
N/A 
Opium 
N/A 
48 hours 
75% excreted in urine within 48 hrs 
Oxycodone 
Xtampza ER 
5.6 hours 
N/A 
Oxycodone Hydrochloride 
Dazidox, Eth-Oxdose, 
Oxaydo, OxyCONTIN, 
OxyCONTIN CR, 
Oxydose, Oxyfast, Oxy 
IR 
147mins-8.9 hrs 
IR: 3.5-4 hrs; Orogastric: 147 mins; 
Conrolled Release: 4.5-8 hrs; Remoxy 
R XR: 8.9 hrs (10mg), 6.62 hrs (20 mg) 
Oxycodone Hydrochloride/ 
Naloxone Hydrochloride 
Targiniq ER 
3.9-17.2 hours 
Oxycodone Hydrochloride: 3.9-5.3 
hours; Naloxone Hydrochloride: 4.1-
17.2 hours 
Oxycodone Hydrochloride/ 
Naltrexone Hydrochloride 
Troxyca ER 
4-13 hours 
Oxycodone Hydrochloride XR: 7.2 hrs; 
Naltrexone Hydrochloride: 4 hrs;  
6-beta-naltrexol: 13 hrs 
Oxycodone 
Hydrochloride/Ibuprofen 
Combunox 
3.1-3.7 hours 
Oxycodone Hydrochloride: 3.1-3.7 
hrs; Ibuprofen: 1.8-2.6 hrs 
Oxymorphone Hydrochloride 
Numorphan, Opana, 
Opana ER 
7.25-9.43 hours 
N/A 
Promethazine Hydrochloride/ 
Codeine Phosphate 
Phenergan w/ Codeine N/A 
N/A 
Promethazine/Phenylephrine/ 
Codeine Phosphate 
Promethacine VC w/ 
Codeine 
2.5-14 hours 
Promethazine: 10-14 hours; 
Phenylephrine: 2.5 hours;  
Codeine Phosphate: 3 hours 
Propoxyphene 
N/A 
6-36 hours 
Compound: 6-12 hrs; Metabolites 
(norpropoxyphene): 30-36 hrs 
Propoxyphene Napsylate/ 
Acetaminophen 
N/A 
6-36 hours 
Compound: 6-12 hrs; Metabolites 
(norpropoxyphene): 30-36 hrs;  
Acetaminophen: 2-4 hours 
Remifentanil Hydrochloride 
Ultiva 
3-10 minutes 
Dose related 
Sufentanil Citrate 
Sufenta, Dsuria 
164 min-13.4 hrs 
Sublingual: 13.4 hrs;  
Injection: 164 minutes 
Tapentadol Hydrochloride 
Nucynta, Nucynta ER 4-5 hours 
  
Tramadol Hydrochloride 
Ultram, Ultram ER, 
Ryzolt, Rybix ODT, 
FusePaq, Synapryn, 
ConZip 
5.6-11 hours 
Depends on release & trade name:  IR: 
5.6-6.7 hrs; ER: 6.5-10 hrs; 
Ryzolt: 6.5 +/- 1.5 hours; Ultram: 7.9 
hrs; ConZip: 10 hrs 
Tramadol 
Hydrochloride/Acetaminophen 
Ultracet 
5-9 hours 
Tramadol Hydrochloride: 5-9 hrs; 
Acetaminophen: 2-3 hrs 


Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 33 of 33 
 
Triprolidine/Pseudoephedrine/ 
Codeine 
Triacin C 
2.1-10 hours 
Triprolidine: 2.1-5 hours; 
Pseudoephedrine: 4-10 hours; 
Codeine: no results