Despite the significant public health burden, there are few pharmacological treatment options, with only 4 medications receiving FDA approval. This contrast between public need and lack of approved pharmacological treatments does not highlight a lack of research; on the contrary, close to 2 dozen potential medications have reached clinical testing. It instead is largely owed to the presence of an expensive and burdensome medications development process, notoriously deemed the “valley of death,” whereby medications fail in their transition from preclinical to initial clinical testing. There is a second “valley of death” where medications fail to translate from early human laboratory efficacy into large-scale, ecologically valid clinical trials. Therefore, the practice quit attempt model aims to develop a novel early efficacy paradigm to more efficiently screen future AUD medication candidates. The present study will utilize naltrexone , an FDA-approved medication for AUD, to serve as an active control to test both the practice quit attempt paradigm and the efficacy of Varenicline . In relation to the former, NTX is an FDA-approved opioid antagonist with high affinity for both the mu-opioid and kappaopioid receptors. With its endogenous opioid blocking effects, NTX has been found to be associated with reduction in both alcohol craving and consumption. These effects make NTX an excellent candidate for the practice quit paradigm. VAR is an FDA-approved medication for smoking cessation that has been associated with reduction in alcohol cravings in previous animal and human laboratory studies.

Based on these findings and in combination with past literature, VAR poses a potential benefit as an AUD pharmacological therapy and,cannabis grow system subsequently, an appropriate experimental medication within the practice quit paradigm. Earlier screening models for phase 2 medication trials largely lack the ecological validity needed to construct clinically meaningful endpoints for treatment-seeking individuals. This practice quit study differs from previous trials in its introduction of a paradigm that displays assay sensitivity via placebo controls, a superiority comparison between an FDA-approved medication and an experimental candidate, increased ecological validity as participants are asked to quit drinking in the real-world and not only evaluated in the laboratory setting, similar to what is seen in large scale RCTs, and an alcohol CR assessment to validate the sensitivity of the paradigm for detecting medication effects. The successful completion of this study will advance medications development by proposing and validating a novel early efficacy model for screening AUD pharmacotherapies, which in turn can serve as an efficient strategy for making go/no-go decisions as to whether to proceed with clinical trials. Specifically, a valid model of initial efficacy will allow us to reliably detect an efficacy signal for AUD pharmacotherapies, and in turn decide whether to proceed to the full-scale efficacy testing.Over 14 million adults in the United States have an AUD; however, only 8% of adults with current AUD received treatment. Only four pharmacotherapies are currently approved by the Food and Drug Administration for the treatment of AUD, and these medications are only modestly effective with number needed to treat ranging from 7–144 across studies. Therefore, there is a clear need to develop more efficacious treatments, particularly those with novel molecular targets.

To that end, the modulation of neuroimmune signaling is a promising AUD treatment target. A growing body of literature indicates that the neuroimmune system may play a critical role in the development and maintenance of AUD, termed the neuroimmune hypothesis of alcohol addiction. In animal models, chronic alcohol consumption induces a neuroimmune response through the activation of microglia and increased expression of pro-inflammatory cytokines and neuronal cell death. Elevated microglial markers have been identified in the postmortem brains of individuals with an AUD, and pro-inflammatory cytokine levels are higher in individuals with AUD compared to controls. Neuroinflammation has also been implicated in mood disorders. Moreover, mood states are considered to be a central feature of AUD, with a negative mood state emerging with increasing AUD severity. Interactions between inflammatory pathways and the neurocircuitry activated in depression and addiction are thought to contribute to negative mood. Therefore, a neuroimmune modulator may treat AUD and related negative mood symptoms through similar pathways. Ibudilast shows promise as a novel AUD pharmacotherapy. IBUD reduced alcohol intake by 50% in two rat models, and selectively decreased drinking in alcohol-dependent mice relative to non-dependent mice. In a human laboratory trial, treatment with IBUD was well-tolerated and resulted in reductions in tonic craving and improvements in mood reactivity to stress and alcohol cue exposure compared to placebo. IBUD is a selective phosphodiesterase inhibitor, with preferential inhibition of PDE3A, PDE4, PDE10A, and PDE11A, and a macrophage migration inhibitory factor inhibitor. Both PDE4 and MIF are involved in neuroinflammatory processes through the regulation of inflammatory responses in microglia, and PDE4Bexpression is upregulated after chronic alcohol exposure. Therefore, IBUD is thought to reduce neuroinflammation through the inhibition of these pro-inflammatory molecules.

IBUD crosses the blood–brain barrier, and is neuroprotective as it suppresses the production of pro-inflammatory cytokines and enhances the production of anti-inflammatory cytokines. While IBUD is a promising AUD pharmacotherapy, its underlying mechanisms of action on the human brain remain largely unknown. PDE4 is highly expressed in neuronal and non-neuronal cells including glia in brain regions associated with reward and reinforcement, including the ventral striatum, and PDE4 can directly regulate dopamine in the striatum in mice. Functional magnetic resonance imaging alcohol cue-reactivity paradigms have commonly been used to evaluate if pharmacological AUD treatments alter brain activation in reward processing circuity. Alcohol cue-elicited reward activation is predictive of treatment response; thus demonstrating that functional neuroimaging can provide mechanistic data for AUD pharmacotherapy development. This may be particularly relevant in the case of IBUD, where the mechanism of action as an AUD treatment is currently unknown, but can be hypothesized to involve the striatum, which is activated in the alcohol cue-reactivity paradigm. Therefore, the present study sought to investigate the efficacy of IBUD to attenuate alcohol cue-elicited VS activation in individuals with AUD. The current study was an experimental medication trial of IBUD compared to placebo in non-treatment-seeking individuals with an AUD. To advance the development of IBUD as an AUD treatment,cannabis grow lights the present study examined the efficacy of IBUD, relative to placebo, to reduce negative mood and reduce heavy drinking as ≥5 drinks/day for men and ≥4 drinks/day for women over the course of 2-weeks. A micro-longitudinal design allowed for daily assessments during the course of treatment. We hypothesized that ibudilast would reduce negative mood and decrease heavy drinking over the course of the study. To investigate the neural substrates underlying IBUD’s action, the present study also examined the effect of IBUD on neural alcohol cue-reactivity. We hypothesized that ibudilast would attenuate alcohol cue-elicited activation in the VS relative to placebo. Finally, this study explored the relationship between neural alcohol cue reactivity in the VS and drinking outcomes.This study was conducted at an outpatient research clinic in a medical center. Participants were recruited through social media and mass transit advertisements. Initial screening was conducted through telephone interview, with eligible participants invited for an in-person assessment. Eligible individuals were between 21 and 50 years old who met criteria for a current DSM-5 mild-to-severe AUD. Participants were required to drink above moderate drinking levels, as defined by the NIAAA as >14 drinks/ week for men and >7 drinks/week for women, in the 30 days prior to screening. Exclusion criteria were: currently receiving or seeking treatment for AUD; past year DSM-5 diagnosis of substance use disorder ; lifetime diagnosis of schizophrenia, bipolar disorder, or any psychotic disorder; non-removable ferromagnetic objects in body; claustrophobia; and serious head injury or prolonged period of unconsciousness . Participants were excluded if they had a medical condition thought to interfere with safe participation and if they reported recent use of medications contraindicated with ibudilast.

Women of a childbearing age had to be practicing effective contraception and could not be pregnant or nursing. See Fig. 1 for the trial enrollment flow.Participants completed a series of assessments for eligibility and individual differences. These measures included the Structured Clinical Interview for DSM-5, the Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised, and the 30-day Timeline Follow back Interview for alcohol, cigarette, and cannabis. Participants also completed assessments regarding their alcohol use, including: Alcohol Use Disorder Identification Test and Alcohol Dependence Scale, which measure severity of alcohol use problems, Penn Alcohol Craving Scaleand Obsessive Compulsive Drinking Scale, which measure alcohol craving, and the Reasons for Heavy Drinking Questionnaire to assess withdrawal-related dysphoria, indicated by question #6: “I drink because when I stop, I feel bad ”. Participants also completed measures of smoking severity and depressive symptomology. At each in-person visit, participants were required to have a breath alcohol concentration of 0.00 g/dl and test negative on a urine toxicology screen for all drugs of abuse . Blood pressure and heart rate were assessed at screening and at each visit. Participants completed three in-person study visits occurring on Day 1 , Day 8 , and Day 15 . Randomization visits occurred on Mondays and Tuesdays to ensure that participants were at the target medication dose by the weekend. Side effects were elicited in open ended fashion and were reviewed by the study physicians . Adverse events were coded using the MedDRA v22.0 coding dictionary. Treatment emergent adverse events were defined as adverse events that started after the first dose of the study drug or worsened in intensity after the first dose of study drug. Participants completed daily diary assessments, reporting on their past-day alcohol use, mood, assessed with a shortened form of the Profile of Mood States, and craving, assessed through a shortened form of the Alcohol Urge Questionnaire. Participants received daily text message reminders with links to these assessments.A set of generalized estimating equations with compound symmetric covariance structure were run in SAS 9.4 to account for repeated measures. GEEs were selected as the analytical method because parameter estimates are consistent even when the covariance structure is mis-specified. As such, a compound symmetric covariance structure was chosen. Of note, due to missing data on all outcome and predictor variables, two participants were naturally excluded via list wise deletion for the GEE analysis. A GEE model was first run to assess the effect of medication on negative mood. The dependent variable, negative mood , was treated as continuous so a normal distribution with identity link function was chosen. A compound symmetric covariance structure was chosen to account for the repeated assessments. Independent variables for these analyses were medication , drinking day , and the interaction of medication by drinking day. Sex, age, depressive symptomology , and smoking status were examined as covariates; only significant covariates were retained in the final model to improve model clarity and ease of replication. A similar model was conducted to assess the effect of medication on craving, with the dependent variable being craving as measured by the AUQ. For both analyses, predicted means, standard errors, and 95% confidence intervals for negative mood and craving were calculated based on final models. The dependent variables for the drinking analyses were binary, such that 1 indicated a heavy drinking day or drinking day and a 0 indicated no heavy drinking or drinking, respectively. A binomial distribution with logit link function was chosen to model the binary dependent variable . Since participants were not on medication at baseline , this time point was excluded from the analysis. Independent variables included in the models were medication , time , and the interaction of medication by time. Baseline drinking information were also included in the model as a control. As above, sex, age, depressive symptomology , and smoking status were examined as covariates; only significant covariates were retained in the final model to improve model clarity and ease of replication. For both analyses, predicted probabilities, standard errors, and 95% confidence intervals for heavy drinking and any drinking were calculated based on final models. A general linear model was used to evaluate the effect of medication on VS activation. The dependent variable was VS percent signal change between ALC and BEV blocks. Medication was the independent variable. Age, sex, depressive symptomology , and smoking status were examined as covariates; only significant covariates were retained in the final model. Finally, to evaluate if VS activation interacted with medication in predicting drinking in the week following the scan, a between-subject factor for VS activation was added to the model, along with a medication by VS activation split interaction.