Clinical trial NCT03489850
Conditions: Alcohol Use Disorder
Sponsor: University of California, Los Angeles
Start date: 2018-07-16; Completion date: 2020-03-31
Enrollment: 52 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
Alcohol use disorder (AUD) is a prevalent and disabling psychiatric disorder with few, and only moderately efficacious, treatment options. Consequently, the identification of novel treatment targets and the development of rigorous laboratory paradigms to screen and optimize novel therapeutics represents a research priority. Ibudilast (IBUD) is a neuroimmune modulator that inhibits phosphodiesterase-4 and -10 and macrophage migration inhibitory factor. Recently in an AUD sample, IBUD was shown to decrease reactivity to a psychological stressor. Furthermore, IBUD was effective in blunting alcohol reward among participants with greater depressive symptoms, a hallmark symptom of protracted withdrawal. Recently, preclinical research in opiates has demonstrated that drug withdrawal is necessary for microglia activation and neuroinflammation in reward networks, suggesting that IBUD may be most effective among patients who experience withdrawal-related dysphoria. Therefore, this proposed study aims to examine withdrawal-related dysphoria as a moderator of IBUD efficacy in the natural environment measured using Daily Diary Assessment (DDA) approaches. To accomplish this aim, participants meeting criteria for AUD and balanced on the presence of withdrawal-related dysphoria will be enrolled in a double-blinded IBUD trial including consisting of two weeks randomized to medication and DDA assessment. The proposed research aims are:

Aim 1: Test whether IBUD reduces basal negative affect in abstinence, and blunts alcohol-related negative reinforcement. It is hypothesized that IBUD will reduce basal levels of negative affect during alcohol abstinence, and in so doing will interfere with alcohol-induced blunting of negative affectivity as captured during naturalistic drinking episodes.

Aim 2: Test whether IBUD attenuates neural alcohol cue-reactivity. It is hypothesized that IBUD will reduce BOLD activation to alcohol cues in mesocorticolimbic reward circuitry.

Aim 3: Test whether withdrawal-related dysphoria moderates the effects of IBUD. It is hypothesized that IBUD will alleviate basal negative affect, interfere with alcohol-induced negative reinforcement and attenuate BOLD activation to alcohol cues only among participants who experience dysphoria in withdrawal.

Aim 4: Test whether neural activation to alcohol cues is predictive of drinking outcomes. It is hypothesized that individuals with higher mesocorticolimbic activation to alcohol cues will report more drinking in the week following the neuroimaging session.

Detailed description:

Arms: arm 1: 20mg BID Days 1-2 50mg BID Days 3-14 arm 2: Matched to active
Interventions: intervention 1: Ibudilast intervention 2: Placebo
Intervention descriptions: intervention 1: Ibudilast (IBUD) is a neuroimmune modulator that inhibits phosphodiesterase-4 and -10 and macrophage migration inhibitory factor. intervention 2: Placebo is matched to ibudilast active medication.

Protocol documents:
Prot_002.pdf: 
IBUD Withdrawal Protocol 
Version 4, 05/10/18  
 
 
Withdrawal-Related Dysphoria as a Moderator of Ibudilast for 
Alcohol Use Disorder 
NCT #: NCT03489850 
Protocol version 4 
May 10, 2018 
 
 


IBUD Withdrawal Protocol 
Version 4, 05/10/18  
Ibudilast Withdrawal Protocol 
Study Design 
 
Specific Aims 
 
Alcohol use disorder (AUD) is a prevalent and disabling psychiatric disorder with few, and only 
moderately efficacious, treatment options. Consequently, the identification of novel treatment targets 
and the development of rigorous laboratory paradigms to screen and optimize novel therapeutics 
represents a research priority. Ibudilast (IBUD) is a neuroimmune modulator that inhibits 
phosphodiesterase-4 and -10 and macrophage migration inhibitory factor. Recently in an AUD sample, 
IBUD was shown to decrease reactivity to a psychological stressor. Furthermore, IBUD was effective in 
blunting alcohol reward among participants with greater depressive symptoms, a hallmark symptom of 
protracted withdrawal. It is still unknown how neuroimmune modulation impacts brain processes that 
underlie alcohol reward processing. Recently, preclinical research in opiates has demonstrated that drug 
withdrawal is necessary for microglia activation and neuroinflammation in reward networks, suggesting 
that IBUD may be most effective among patients who experience withdrawal-related dysphoria. 
Therefore, this proposed study aims to examine withdrawal-related dysphoria as a moderator of IBUD 
efficacy in the natural environment measured using Daily Diary Assessment (DDA) approaches. To 
accomplish this aim, participants meeting criteria for AUD and balanced on the presence of withdrawal-
related dysphoria will be enrolled in a double-blinded IBUD trial including consisting of two weeks 
randomized to medication, DDA assessment, and fMRI. The proposed research aims are:  
Aim 1: Test whether IBUD reduces basal negative affect in abstinence and blunts alcohol-related 
negative reinforcement. It is hypothesized that IBUD will reduce basal levels of negative affect during 
alcohol abstinence, and in so doing will interfere with alcohol-induced blunting of negative affectivity as 
captured during naturalistic drinking episodes. 
Aim 2: Test whether IBUD attenuates neural alcohol cue-reactivity. It is hypothesized that IBUD will 
reduce BOLD activation to alcohol cues in mesocorticolimbic reward circuitry. 
Aim 3: Test whether withdrawal-related dysphoria moderates the effects of IBUD. It is hypothesized that 
IBUD will alleviate basal negative affect, interfere with alcohol-induced negative reinforcement and 
attenuate BOLD activation to alcohol cues only among participants who experience dysphoria in 
withdrawal. 
Aim 4: Test whether neural activation to alcohol cues is predictive of drinking outcomes. It is 
hypothesized that individuals with higher mesocorticolimbic activation to alcohol cues will report more 
drinking in the week following the neuroimaging session. 
 
Participants 
Participants (N = 50) will be men and women ages 21 to 45 recruited in Los Angeles. Participants will be 
screened for inclusion and exclusion criteria by online and telephone screens which will also assess for 
withdrawal-related dysphoria. Eligible participants will then complete an in-person behavioral screen 
where they will complete self-report assessments of demographics, psychological state/history, and 
alcohol and drug use. A Masters level clinician will complete a structured diagnostic interview (SCID) and 
assess for physiological withdrawal (CIWA).  
 
Inclusion Criteria 
(1) Age between 21 and 45  


IBUD Withdrawal Protocol 
Version 4, 05/10/18  
(2) Meet DSM-5 criteria for current Moderate-to-Severe AUD 
(3) Current Heavy Drinking (> 14 drinks per week for men; > 7 drinks per week for women), as 
indicated by self-reported drinking for the 30 days prior to screening 
(4) Have reliable internet access 
 
Exclusion Criteria 
(1) Currently receiving or seeking treatment for AUD* 
(2) Past year DSM-5 diagnosis of any substance use disorder other than alcohol or nicotine 
(3) A lifetime diagnosis of schizophrenia, bipolar disorder, or any psychotic disorder 
(4) Current use of drugs, other than marijuana, verified by a urine toxicology screen* 
(5) Pregnant, nursing, or refusal to use reliable birth control (if female)* 
(6) A medical condition that may interfere with safe participation (e.g., unstable cardiac, renal, or 
liver disease, uncontrolled hypertension, diabetes, or AST, ALT, or GGT ≥ 3 times upper normal 
limit) 
(7) Self-reported recent (i.e. past 30 day) use of medications that are contraindicated with 
ibudilast* 
(8) Non-removable ferromagnetic objects in body 
(9) Claustrophobia 
(10) Serious head injury or prolonged period of unconsciousness (>30 minutes) 
 
* Participants who meet these criteria at any point during the course of the study (i.e. after 
randomization) will be withdrawn from the study for safety purposes. 
 
Stratification 
To test the role of withdrawal-related dysphoria, participants will be balanced on their yes/no response 
to the item “I drink because when I stop, I feel bad (I am nervous, irritable, and I sleep poorly)" taken 
from the Reasons for Heavy Drinking Questionnaire (RHDQ). 
 
Medication Administration 
Study medication and matched placebo will be provided by Medicinova Inc. and dispensed by the UCLA 
Health Pharmacy Investigational Drug Section who will also maintain the blind. After establishing 
eligibility, participants will be provided with their first medication dosage in a blister-pack. IBUD will be 
provided in 10 mg capsules and participants will be titrated as follows: 20 mg BID during days 1-2, 50 mg 
BID during days 3-14, or matched placebo. All capsules will contain a riboflavin (50 mg) tracer to verify 
compliance. EMA assessment will begin on Medication Day 1 and will continue through Day 15 (one day 
post medication termination). 
 
Measures 
Behavioral Screen Measures 
1. Breathalyzer 
2. Urine toxicology screen and pregnancy test (if female) 
3. The Structured Clinical Interview for DSM-5 (SCID)  
4. Drink Years Assessment (DYA) 
5. The Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised (CIWA-Ar).   


IBUD Withdrawal Protocol 
Version 4, 05/10/18  
6. A 30-day alcohol, cigarette, and marijuana timeline follow-back (TLFB)  
7. Reasons for Heavy Drinking Questionnaire (RHDQ)  
8. Reward-Relief Drinking Scale (RRDS) 
9. The Alcohol Dependency Scale (ADS) 
10. The Alcohol Use Disorders Identification Test (AUDIT) 
11. Family Tree Questionnaire (FTQ) 
12. Fagerstrom Test for Nicotine Dependence (FTND) 
13. Beck Depression Inventory-II (BDI-II) 
14. Pain Catastrophizing Scale (PCS) 
15. Graded Chronic Pain Scale (GCPS) 
16. RTC Ladder 
17. Snaith-Hamilton Pleasure Scale (SHAPS) 
18. Penn Alcohol Craving Scale (PACS) 
19. Obsessive Compulsive Drinking Scale (OCDS) 
20. Concomitant Medications Assessment 
21. Center for Cognitive Neuroscience MRI Safety Screening Form 
 
Physical Exam Measures 
1. Breathalyzer 
2. Urine toxicology screen and pregnancy test (if female) 
3. The Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised (CIWA-Ar).   
4. Timeline follow-back (TLFB) 
5. Concomitant Medications Assessment  
 
Baseline Visit Measures 
1. Breathalyzer 
2. Urine toxicology screen and pregnancy test (if female) 
3. The Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised (CIWA-Ar).   
4. Timeline follow-back (TLFB)  
5. Beck Depression Inventory-II (BDI-II) 
6. Graded Chronic Pain Scale (GCPS) 
7. Snaith-Hamilton Pleasure Scale (SHAPS) 
8. Penn Alcohol Craving Scale (PACS) 
9. Obsessive Compulsive Drinking Scale (OCDS) 
10. Side Effects Checklist (SAFTEE) 
11. Open Ended Side Effects Interview 
12. Concomitant Medications Assessment 
 
Midpoint Measures 
1. Breathalyzer 
2. Urine toxicology screen and pregnancy test (if female) 
3. The Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised (CIWA-Ar).   
4. Timeline follow-back (TLFB)  
5. Beck Depression Inventory-II (BDI-II) 


IBUD Withdrawal Protocol 
Version 4, 05/10/18  
6. Graded Chronic Pain Scale (GCPS) 
7. Snaith-Hamilton Pleasure Scale (SHAPS) 
8. Penn Alcohol Craving Scale (PACS) 
9. Obsessive Compulsive Drinking Scale (OCDS) 
10. Side Effects Checklist (SAFTEE) 
11. Open Ended Side Effects Interview 
12. Concomitant Medications Assessment 
13. Center for Cognitive Neuroscience MRI Safety Screening Form 
 
Midpoint Functional Brain Imaging 
After the midpoint measures (above) are collected, participants will complete a brain functional 
magnetic resonance imaging (fMRI) scan. The scanning will be performed at the Brain Mapping Center 
or at the Center for Cognitive Neuroscience, both located on the UCLA campus. They will be asked to lie 
down on a padded table, with their head placed in the center of a large, metal doughnut-shaped 
magnet.  While the machine is running, the participant will hear loud banging noises and will be offered 
earplugs to reduce the noise made by the magnet.  Head and back support will also be provided to 
minimize discomfort.   
 
Participants will be asked to have a normal night’s sleep the night before the scan, have no more than 
one alcoholic drink, and abstain from drinking coffee within 2 hours prior to the scan. Breath alcohol 
concentration must be zero and for female participants the pregnancy test must be negative 
immediately prior to the scanning session. Prior to scanning, subjects will practice the task in front of a 
computer monitor with headphones and a button response box using the same stimulus delivery 
software as for fMRI scanning. The subjects will practice until it can be ascertained that they understand 
the task. 
 
During the imaging session we will utilize several structural and functional scans. The structural protocol 
will consist of a high-resolution, matched-bandwidth (MBW) scan and a structural magnetization-
prepared rapid-acquisition gradient echo (MPRAGE) scan. The functional scan will include a fMRI 
alcohol-cue reactivity paradigm previously reported to elicit blood oxygen level dependent (BOLD) 
response in mesocorticolimbic regions (Schacht et al., 2011).  
 
The visual alcohol cue reactivity paradigm is a well-validated, with strong reliability and within-
participant stability (1). The alcohol cues task consists of viewing alcohol and neutral cues, developed 
based on work by Schacht and colleagues (2011). There are five types of visual cues: alcoholic 
beverages, non-alcoholic (neutral) beverages, blurred alcoholic beverages, blurred non-alcoholic 
beverages, and a fixation cross. Stimuli are presented in six 120-s epochs (total scan duration: 12 
minutes), with each epoch consisting of four 24-s blocks (one block of alcohol cues, one block of neutral 
cues, one block of blurred images, and one block of fixation). During each 24-s block, 5 individual 
pictures will be displayed for ~4.8 seconds each. Alcohol blocks will be specific to beverage type (beer, 
wine, or liquor), with two blocks of each beverage type. Each block will be followed by a 6-s washout 
period, which allows the hemodynamic response from the previous block to decline.  


IBUD Withdrawal Protocol 
Version 4, 05/10/18  
Prior to scanning, participants will be shown how to provide ratings of their cravings using an optically 
isolated universal serial bus (USB) interface, which consists of a four-button (1= low to 4= high) response 
box. Subjects will provide ratings of their craving immediately following each cue block. 
 
Example Epoch of Alcohol Cue Reactivity Paradigm 
 
Final Measures 
1. Breathalyzer 
2. Urine toxicology screen, ETG test, and pregnancy test (if female) 
3. The Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised (CIWA-Ar).   
4. Timeline follow-back (TLFB)  
5. Beck Depression Inventory-II (BDI-II) 
6. Graded Chronic Pain Scale (GCPS) 
7. Snaith-Hamilton Pleasure Scale (SHAPS) 
8. Penn Alcohol Craving Scale (PACS) 
9. Obsessive Compulsive Drinking Scale (OCDS) 
10. Side Effects Checklist (SAFTEE) 
11. Open Ended Side Effects Interview 
12. Concomitant Medications Assessment 
 
DDA Measures  
• 
See attached IBUD DDA Codebook 
 
 
 
 
 


IBUD Withdrawal Protocol 
Version 4, 05/10/18  
Study Design and Flow Chart 
 
 

SAP_003.pdf: 
IBUD Withdrawal Analysis Plan 
Version 3, May 10, 2018 
 
 
 
Withdrawal-Related Dysphoria as a Moderator of Ibudilast for 
Alcohol Use Disorder 
NCT #: NCT03489850 
Statistics and Data Analysis Plan 
Version 3 
May 10, 2018 
 
 


IBUD Withdrawal Analysis Plan 
Version 3, May 10, 2018 
 
Ibudilast and Withdrawal-Related Dysphoria: 
Statistics and Data Analysis 
 
Power Analysis 
Given the exploratory 
nature of this proposed study, 
power analyses were conducted to 
determine statistical power to 
detect a medium effect size 
(Cohen’s d = 0.5) for the main effect 
of medication. Power calculations 
were conducted using the method 
described in Scherbaum & Ferreter 
(2009) which was developed for the 
multilevel modeling framework 
necessitated in this study where 
DDA observations are nested within 
subjects [1]. Specifically, power was 
calculated based on (1) γ01 = 0.5 
which represents a medium effect 
size for the main effect of medication, (2) 15 daily observations per subject, and (3) an ICC = 0.5, which is 
in line with a previous study from our laboratory which assessed mood and craving outcomes in a 
substance using sample. Based on these calculations (see Figure), the canonical 80% power is achieved 
at N = 70. However, pilot funding for this project unfortunately does not permit us to enroll more than 
50 subjects. Given this maximal feasible N of 50, power was still adequate at 68%.  
 
Interim Analysis 
No interim analysis is planned.  
 
Data analysis plan 
Random Assignment Checks: To confirm the efficacy of random assignment and to check for medication 
group equivalence across demographics, drinking history, mood, alcohol use disorder severity and other 
relevant baseline measures we will perform t-tests on continuous items and χ2 tests of categorical 
items. Variables on which the two groups are unequal at pretest will be covariates in subsequent 
analyses. Medication groups will be stratified on the primary hypothesized mediator, withdrawal-related 
dysphoria to ensure equal representation.  
 
Aim 1: IBUD effects on basal negative affect and alcohol negative reinforcement 
0
0.2
0.4
0.6
0.8
1
20
30
40
50
60
70
80
Power
Subjects
Medication Main Effect Power Calculations
d = 0.5, nobservatios = 15, and ICC=0.5
When N = 50,
Power = 68%
Power for γ01


IBUD Withdrawal Analysis Plan 
Version 3, May 10, 2018 
 
To test the effects of IBUD on basal negative affect in alcohol abstinence a series of multilevel models 
will be conducted on daily reports of negative mood collected with DDA. For this sample, only data 
collected at target IBUD dose (day 3 and beyond) and outside of the context of an alcohol drinking 
occasion will be used. Multilevel modeling will be employed to account for the nested nature of the data 
where observations are nested within subjects. In particular, we will analyze the effects of Medication, a 
two-level between-subjects factor, on self-reported negative affect taken from the POMS. Covariates 
will be: BDI-II score (a between-subjects variable), and any unbalanced demographic factors identified in 
the Preliminary analyses outlined above. All analyses will be conducted in R version 3.3.0 [2]. Multilevel 
models will be estimated using the lme function in the multilevel package [3]. This approach is 
consistent with our previous work in pharmacotherapy studies [4], [5]. 
 
IBUD effects on alcohol-related negative reinforcement will be tested through examining self-reported 
effects of alcohol in the context of a naturalistic drinking event. In this DDA study participants are 
trained to report when they drank alcohol the previous day and how the alcohol affected their mood 
and craving. In this study negative reinforcement will be operationalized by reductions in self-reported 
negative affect after consumption of alcohol. Based on the reported magnitude and duration of drinking 
we will estimate participants breath alcohol concentrations (BrAC’s). To analyze alcohol’s effects on 
negative affect, a series of multilevel models will test the effects of Medication (a 2-level between 
subjects factor), Alcohol (a binary within-subjects factor capturing mood “before,” or “while” drinking), 
and the Medication × Alcohol interaction on the dependent variable, negative affect from the POMS. A 
significant Medication × Alcohol interaction will be interpreted in terms of IBUD affecting alcohol’s 
negatively reinforcing effects.  
 
Aim 2: IBUD effects on neural alcohol cue-reactivity 
IBUD’s effects on neural alcohol cue-reactivity will be analyzed using FSL’s FEAT 
(https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL). The following preprocessing steps will be used: 1) FSL’S 
MCLFIRT will be used for motion correction; 2) FSL’s BET will be used for non-brain tissue and skull 
removal; 3) Data will be spatially smoothed using a Gaussian kernel of FWHM 8mm; 4) Data will be 
mean-based intensity normalized; and 5) Data will undergo highpass temporal filtering. Explanatory 
variables will be created by convolving the stimulus timing files with a double gamma hemodynamic 
response function. Contrast maps will be created by contrasting alcohol and neutral beverage cues. 
After transforming the masks into standard MNI space, higher-level analysis will be carried out and Z-
statistic images will be thresholded using GRF-theory-based maximum height thresholding with a 
significance of one-tailed p<0.05. Hierarchical linear modeling (HLM) will be used to test for a 
medication effect. Cue type will be denoted by dummy-coded variables representing contrasts between 
alcohol and neutral beverage cues. The second level predictor will be medication condition (IBUD vs. 
placebo). The main approach is to test, at the whole-brain significance level, the effects of medication 
condition on BOLD activation generated during the alcohol cue reactivity paradigm. 
 
Aim 3: Moderating Effects of Withdrawal-Related Dysphoria 


IBUD Withdrawal Analysis Plan 
Version 3, May 10, 2018 
 
To test whether the presence of withdrawal-related dysphoria (WRD) predicts a greater response to 
IBUD, a series of multilevel models will be conducted building on the models analyzed in Aim 1. 
Specifically, in these models we will test the moderating effects of WRD, a binary between-subjects 
factor on IBUD effects through the inclusion of the Medication × WRD interaction. A significant 
interaction between Medication × WRD, or Medication × WRD × Alcohol will be interpreted as evidence 
that the presence of withdrawal related dysphoria is an important predictor of the clinically relevant 
effects of IBUD.   
 
Aim 4: Neural Cue Reactivity as a Predictor of Future Drinking 
To test whether neural alcohol cue reactivity can predict drinking in the week following neuroimaging, 
we will conduct a correlational analysis. We will calculate the number of drinks per drinking day for the 7 
days following the Day 8 mid-point neuroimaging visit from the DDA data. We will also derive the 
maximum percent signal change from the whole brain fMRI analyses using Featquery (part of FSL’s 
FEAT). This signal will be derived from the alcohol versus control cue contrast. For Primary Aim 2a, we 
will compute correlations between the number of drinks per drinking day and the whole brain percent 
signal change values using SAS Statistical Software.  
 
Exploratory Aims: 
Additional models analogous to those outlined for Aim 1 and Aim 3 will be conducted on the following 
outcomes: Positive Mood (POMS) and Craving (AUQ) assessed in daily diary reports, Withdrawal-Related 
Dysphoria (RHDQ) during alcohol abstinence, and Stimulation and Sedation (B-BAES). 
 
Citations 
 
[1] Charles A. Scherbaum and Jennifer M. Ferreter, “Estimating Statistical Power and Required Sample 
Sizes for Organizational Research Using Multilevel Modeling,” Organ. Res. Methods, vol. 12, no. 2, 
pp. 347–367, Apr. 2009. 
[2] R Development Core Team, R: A Language and Environment for Statistical Computing. Vienna, 
Austria: R Foundation for Statistical Computing, 2008. 
[3] P. Bliese, multilevel: Multilevel Functions. 2013. 
[4] L. A. Ray, S. Bujarski, P. F. Chin, and K. Miotto, “Pharmacogenetics of Naltrexone in Asian Americans: 
A Randomized Placebo-Controlled Laboratory Study,” Neuropsychopharmacology, vol. 37, no. 2, pp. 
445–455, Jan. 2012. 
[5] S. Bujarski, J. MacKillop, and L. A. Ray, “Understanding naltrexone mechanism of action and 
pharmacogenetics in Asian Americans via behavioral economics: A preliminary study.,” Exp. Clin. 
Psychopharmacol., vol. 20, no. 3, pp. 181–190, Jun. 2012. 
[6] R. Miranda et al., “Biobehavioral mechanisms of topiramate’s effects on alcohol use: an 
investigation pairing laboratory and ecological momentary assessments,” Addict. Biol., p. n/a-n/a, 
Nov. 2014.