Ibudilast attenuates the association between pain intensity and heavy drinking in alcohol use disorder: A secondary analysis of a randomized clinical trial

Abstract

Background:

Pain frequently co-occurs with alcohol use disorder (AUD) and may influence treatment response. In this secondary analysis of a completed randomized clinical trial, we examined whether baseline pain intensity and pain-related disability moderate the effects of ibudilast, a neuroimmune-modulating agent, on drinking outcomes over 12 weeks.

Method:

Participants (N = 102; 60 % male; M age = 44.26, SD = 10.81) with moderate-to-severe AUD were randomized to ibudilast (50 mg twice daily) or placebo for 12 weeks. Pain intensity and disability were assessed at baseline using the Graded Chronic Pain Scale. Piecewise linear mixed-effects models tested the moderating effect of pain on percent heavy drinking days (PHDD), percent days abstinent, drinks per day, and drinks per drinking day, adjusting for early (baseline–Week 2) and late (Weeks 4–12) trial phases.

Results:

Pain variables were not significantly correlated with any drinking outcome at baseline (all |r| ≤ .08, p > .45). Significant three-way interactions (Medication × Pain Intensity × Time) emerged for PHDD in both early and late phases (ps < 0.05), indicating that higher pain intensity predicted greater PHDD in the placebo arm (b = 0.10, p = .04) but not in the ibudilast arm (b = 0.007, p ≥ .89). Similar interactions were observed for pain intensity and pain-related disability on drinks per day, though simple slopes were nonsignificant. No significant moderation effects were found for percent days abstinent or drinks per drinking day.

Conclusions:

Ibudilast attenuated the positive association between baseline pain intensity and heavy drinking, suggesting that pain intensity may identify an AUD subgroup more likely to benefit from neuroimmune-targeted pharmacotherapy. These findings support incorporating pain phenotyping into AUD treatment research and point toward a precision medicine approach to enhance treatment efficacy.

1. Introduction

The presence of pain symptoms is a prevalent and clinically important issue among individuals with alcohol use disorder (AUD), with significant implications for treatment response and outcomes. Epidemiological and clinical data suggest that approximately 40 %–60 % of individuals with AUD report persistent or chronic pain (Larson et al., 2007; Witkiewitz et al., 2015). This co-occurrence is associated with heightened alcohol craving, more severe dependence, greater affective distress, and poorer outcomes in both behavioral and pharmacological treatment trials (Brennan et al., 2005; Jakubczyk et al., 2015; Nieto et al., 2021). The limited success of existing pharmacotherapies for AUD may be due in part to unaddressed factors like pain, as overall effect sizes for medications like naltrexone and acamprosate remain modest and relapse rates high across clinical trials (Jonas et al., 2014), underscoring the need for more targeted and effective treatment strategies.

One promising approach is to identify individual-level moderators that influence treatment response, thereby informing precision medicine efforts in AUD (Litten et al., 2016). Pain is a compelling candidate in this regard, given evidence that it not only exacerbates AUD severity but may also interact with the neurobiological mechanisms underlying treatment efficacy. Specifically, pain and problematic alcohol use appear to share overlapping pathophysiological substrates including dysregulated neuroimmune signaling, glial activation, and alterations in mesocorticolimbic reward circuitry (Edwards et al., 2020; Egli et al., 2012; Elman et al., 2016; Navratilova et al., 2015). These converging mechanisms raise the possibility that individuals with co-occurring AUD and pain symptoms may be especially responsive to pharmacotherapies that target neuroimmune pathways, such as those under development for inflammatory or neurodegenerative disorders.

Ibudilast is a selective phosphodiesterase (PDE 3, 4, 10, 11) inhibitor and glial cell modulator with anti-inflammatory and neuroprotective properties (Rolan et al., 2009). In preclinical models of alcohol dependence, ibudilast reduces alcohol self-administration and cue-induced reinstatement (Bell et al., 2015). In early-phase human studies, it has shown potential in reducing alcohol craving, enhancing mood, and modulating neural responses to alcohol cues (Grodin et al., 2021; Ray et al., 2017). However, a recent 12-week randomized controlled trial of ibudilast for AUD found no significant effects on drinking outcomes in the overall sample (Ray et al., 2025). Given the biological plausibility of ibudilast’s mechanisms and the heterogeneity of AUD presentations, secondary analyses examining individual-level moderators are warranted. To our knowledge, no prior study has tested whether pain symptoms moderate responses to ibudilast for drinking reduction.

The present study addresses this gap by conducting a secondary analysis of data from the aforementioned RCT (Ray et al., 2025), testing whether pain intensity and pain-related disability moderate treatment response to ibudilast. Pain levels were assessed at baseline using the Graded Chronic Pain Scale, a validated multidimensional measure that captures both pain intensity and pain-related disability (Von et al., 1992). The primary outcomes were percent heavy drinking days (PHDD), percent days abstinent, drinks per day, and drinks per drinking day collected across the 12-week treatment period. We hypothesized that pain intensity would moderate the effects of ibudilast on changes in drinking behavior over time. Specifically, we expected that baseline pain intensity would moderate the effects of ibudilast on drinking trajectories, such that higher pain would predict greater reductions in percent heavy drinking days under ibudilast compared to placebo. This study aims to clarify whether pain is a clinically meaningful moderator of response to neuroimmune-based pharmacotherapy, thereby advancing efforts to personalize treatment strategies for individuals with AUD.

2. Materials and methods

2.1. Study design and procedures

This study is a secondary analysis of a completed Phase 2, 12-week, double-blind, placebo-controlled randomized clinical trial (ClinicalTrials.gov identifier: NCT03594435) evaluating the efficacy of ibudilast (50 mg BID) for AUD. Participants (N = 102) were randomized to ibudilast (n = 53) or matched placebo (n = 49) and completed study visits at baseline and Weeks 2, 4, 6, 8, 10, and 12. Some assessments were conducted remotely due to COVID-19. The parent trial was conducted at UCLA, approved by the institutional review board, and monitored by a data and safety board. Participants provided written informed consent. Enrollment occurred between October 2018 and April 2023.

2.2. Participants

Eligible participants were adults aged 18–65 who met DSM-5 criteria for moderate-to-severe AUD consumed ≥14 (men) or ≥7 (women) drinks per week and were actively seeking treatment. Exclusion criteria included other recent substance use disorders (except nicotine or mild cannabis use disorder), serious psychiatric or medical conditions, contraindicated medications, and current use of most psychotropic drugs. Diagnostic eligibility was assessed using the Structured Clinical Interview for DSM-5 (SCID-5) (First, , 2014), alongside a medical screening visit.

Participants were randomized 1:1 to receive ibudilast or placebo using stratified block randomization based on sex and heavy drinking status. The ibudilast dose was titrated to 50 mg BID by Day 3 and tapered at the end of Week 12. All participants received a computer-delivered behavioral module designed to support alcohol use reduction.

2.3. Measures

This secondary analysis focused on biweekly Timeline Follow-Back (First, , 2014) assessments of percent heavy drinking days, drinks per day, drinks per drinking day, and percent days abstinent. Baseline chronic pain was assessed using the Graded Chronic Pain Scale, a well-validated instrument for grading pain severity (Von et al., 1992). Following the validated scoring methods for the scale (Von et al., 1992), two key continuous variables were derived for the analyses. The pain intensity score (range 0–100) was calculated by taking the mean of three items assessing pain ‘right now,’ ‘worst pain in the past 3 months,’ and ‘average pain in the past 3 months’ (each rated 0–10) and multiplying the result by 10. Similarly, the pain-related disability score (range 0–100) was derived from the mean of three items assessing the degree to which pain interfered with daily, social, and work activities in the past 3 months (each rated 0–10), with the result also multiplied by 10.

2.4. Statistical analysis

Baseline scores for pain intensity and pain-related disability were highly right-skewed, with a large proportion of participants reporting zero or very low levels of pain intensity/disability. To better approximate a normal distribution and meet the assumptions of the statistical models, a log transformation was applied. Because the data contained zero values, a small constant of 0.5 was added to each score before the base-10 logarithm was calculated. Pearson correlations were calculated among baseline pain intensity and drinking variables using the Hmisc package in R.

Longitudinal drinking outcomes were analyzed using piecewise linear mixed-effects models implemented in R using the lme4 and lmerTest packages. Time was modeled in two linear segments: baseline to Week 2 (Time1) and Weeks 4–12 (Time2). The decision to use a piecewise model was theory-driven and based on common patterns observed in pharmacotherapy trials for alcohol use disorder (Ray et al., 2021), including the parent trial (Ray et al., 2025). Often, there is an initial, rapid change in drinking when participants start a trial (the “acute phase”), followed by a different, more stable trajectory as they adapt to the treatment over the longer term (the “sustained phase”). A standard linear model assumes a single rate of change, which can mask these distinct phase effects. Our piecewise model allows us to flexibly and more accurately capture these two different slopes: an early trajectory from baseline to Week 2 (Time1), and a separate late trajectory from Week 4 to Week 12 (Time2). Models included fixed effects for medication group (ibudilast vs. placebo), time (Time1 and Time2), baseline log-transformed pain intensity or pain-related disability, and all two- and three-way interactions, along with random intercepts and slopes for subjects. This structure allowed for testing whether baseline pain moderated changes in drinking trajectories over time and whether this effect differed by treatment group. For visualization of the predicted trajectories, participants were stratified into “High Pain” and “Low Pain” groups based on scores of ±1 standard deviation from the mean of the log-transformed baseline pain intensity score. No covariates were included in the primary longitudinal models. The successful randomization of participants to the treatment and placebo groups resulted in well-balanced baseline demographic and clinical characteristics, including baseline drinking severity. Therefore, covariates were not required to obtain an unbiased estimate of the treatment effect.

Simple slope analyses were conducted using the emmeans package to probe significant interactions. Statistical significance was determined at α < 0.05 for all tests. No correction for multiple comparisons was applied given that all outcomes tested the common hypothesis that pain intensity would moderate the effect of ibudilast on alcohol drinking.

3. Results

3.1. Sample characteristics

The analytic sample included 102 participants (60 % male, M_age = 44.26, SD = 10.81). Mean pain intensity at baseline was 21.22 (SD = 20.05) for participants in the placebo group (n = 49) and 25.16 (SD = 24.52) for participants in the ibudilast group (n = 53). On the Graded Chronic Pain Scale, these scores correspond to low pain intensity (Von et al., 1992). Mean pain-related disability at baseline was 11.56 (SD = 18.93) for participants in the placebo group and 15.28 (SD = 22.79) for participants in the ibudilast group. Pain-related disability was minimal across the sample; most participants in both the placebo (67.3 %) and ibudilast (69.8 %) groups reported zero days kept from usual activities due to pain. Regarding pain duration over the prior 3 months, the placebo group reported an average of 20.61 (SD = 41.79) pain days, while the ibudilast group reported 27.98 (SD = 51.18) pain days. At baseline, participants in the placebo group had an average PHDD of 65.7 % (SD = 34.9 %) and drinks per day was 5.09 (SD = 3.28), whereas participants in the ibudilast group had an average PHDD of 67.5 % of days (SD = 34.5 %) and drinks per day was 5.56 (SD = 3.8). Treatment groups did not differ significantly on baseline demographics, alcohol use measures, or pain intensity (all ps > 0.10). Full demographic and clinical characteristics for this sample have been previously reported in (Ray et al., 2025).

3.2. Pain and alcohol use correlations

Pearson correlation analyses revealed that neither baseline pain intensity nor pain-related disability was significantly associated with any drinking outcome at baseline. Specifically, pain intensity was unrelated to PHDD (r = 0.08, p = .45), percent days abstinent (r = 0.03, p = .75), drinks per drinking day (r = 0.02, p = .88), and drinks per day (r = −0.05, p = .65). Similarly, null associations were found for pain-related disability across all drinking outcomes (all |r| ≤ .07, ps ≥ 0.48). These findings suggest that neither pain intensity nor disability were associated with baseline drinking behavior, supporting the interpretation that any observed moderation effects likely reflect differential change over time rather than preexisting differences in drinking severity.

3.3. Moderator analyses

Percent heavy-drinking days (PHDD)

Pain Intensity.

PHDD declined during Time1 (b = −0.27, SE = 0.07, t(95.3) = −3.89, p < .001, 95 % CI [−0.41, −0.14]) and increased in Time2 (b = 0.25, SE = 0.08, t(94.4) = 3.25, p = .002, 95 % CI [0.10, 0.41]). The three-way interactions indicated that ibudilast attenuated the chronic pain→PHDD slope in both phases (Time1: Med × log pain intensity × Time1, b = −0.18, SE = 0.08, t(96.5) = −2.21, p = .029, 95 % CI [−0.34, −0.02]; Time2: Med × log pain intensity × Time2, b = 0.19, SE = 0.09, t(95.2) = 2.08, p = .040, 95 % CI [0.01, 0.37]).

Simple-slope analyses (Fig. 1A) showed that, across both early and late phases, higher baseline pain intensity predicted greater PHDD in the placebo arm (b = 0.10, SE = 0.05, t(95) = 2.03, p = .045, 95 % CI [0.002, 0.202]) but was unrelated to drinking under ibudilast (slope = 0.007, SE = 0.05, p = .89). The plot of the predicted trajectories (Fig. 1B) illustrates this finding, showing that the divergence in heavy drinking between high- and low-pain individuals under placebo was not present in the ibudilast group.

Fig. 1.

Ibudilast Attenuates the Association Between Baseline Pain Intensity and Heavy Drinking Trajectories. (A) Simple slopes of baseline pain intensity predicting percent heavy drinking days (PHDD) during the early (Baseline to Week 2) and late (Week 4 to Week 12) trial phases. Points represent the unstandardized slope estimates derived from the primary piecewise mixed-effects model for the placebo (orange circles) and ibudilast (green triangles) groups; vertical bars denote 95 % confidence intervals. (B) Model-predicted trajectories of PHDD for subgroups based on the same model. “High Pain” (green) and “Low Pain” (orange) groups represent ±1 standard deviation from the mean of the log-transformed baseline pain score. Solid and dotted lines represent the placebo and ibudilast groups, respectively. Together, the panels illustrate that the positive association between pain intensity and heavy drinking observed in the placebo group was attenuated among participants receiving ibudilast.

Pain-Related Disability.

When pain-related disability was tested as a moderator, there were significant main effects for the early phase (Time1: b = −0.23, SE = 0.06, t(211.7) = −4.16, p < .001) and the late phase (Time2: b = 0.22, SE = 0.06, t(163.6) = 3.42, p < .001). Main effects for Medication (b = 0.09, SE = 0.09, t(95.5) = 1.11, p = .27) and pain-related disability (b = 0.07, SE = 0.07, t(95.1) = 1.07, p = .29) were not significant. None of the two-way interactions were significant (all ps > 0.22). The three-way Med × log pain disability × Time interactions were not significant for either the early phase (b = −0.13, SE = 0.08, t (211.3) = −1.66, p = .10) or the late phase (b = 0.13, SE = 0.09, t (163.1) = 1.49, p = .14).

Percent days abstinent

Pain Intensity.

Percent days abstinent increased in the early phase (Time1: b = 0.274, SE = 0.058, t(93.1) = 4.71, p < .001, 95 % CI [0.160, 0.387]) and decreased in the late phase (Time2: b = −0.255, SE = 0.063, t(92.8) = −4.03, p < .001, 95 % CI [−0.378, −0.131]). No significant pain main effect or interactions with medication and phase were observed (all p > .60).

Pain-Related Disability.

The model revealed significant main effects for the early phase (Time1: b = 0.28, SE = 0.05, t(93.7) = 5.89, p < .001) and the late phase (Time2: b = −0.26, SE = 0.05, t(93.2) = −5.14, p < .001). Main effects for Medication (b = 0.01, SE = 0.08, t(94.6) = 0.07, p = .95) and pain-related disability (b = −0.00, SE = 0.06, t(94.2) = −0.01, p = .99) were not significant. The three-way interactions were not statistically significant for either the early phase (b = 0.11, SE = 0.07, t(93.0) = 1.69, p = .095) and the late phase (b = −0.12, SE = 0.07, t(92.6) = −1.69, p = .094).

Drinks per day

Pain Intensity.

Drinks per day decreased during Time1 (b = −3.32, SE = 0.64, t(96.1) = −5.21, p < .001, 95 % CI [−4.57, −2.07]) and increased in Time2 (b = 3.13, SE = 0.64, t(94.7) = 4.87, p < .001, 95 % CI [1.87, 4.39]). Both pain intensity × Time (Time1: b = 1.12, SE = 0.53, t(97.8) = 2.10, p = .039, 95 % CI [0.07, 2.16]; Time2: b = −1.10, SE = 0.54, t(96.4) = −2.04, p = .044, 95 % CI [−2.15, −0.04]) and three-way Med × log pain intensity × Time interactions (Time1: b = −1.65, SE = 0.74, t(98.0) = −2.23, p = .028, 95 % CI [−3.09, −0.20]; Time2: b = 1.71, SE = 0.74, t(96.4) = 2.30, p = .024, 95 % CI [0.25, 3.17]) were significant suggesting that ibudilast attenuated the link between pain intensity and drinks per day observed in the placebo group. Simple slopes analyses for drinks per day showed that pain did not significantly predict drinks per day in either arm at either phase (all p > .67; 95 % CIs include zero).

Pain-Related Disability.

The model for pain-related disability revealed significant main effects for the early phase (Time1: b = −2.84, SE = 0.52, t(98.4) = −5.49, p < .001) and the late phase (Time2: b = 2.67, SE = 0.52, t(96.7) = 5.14, p < .001). Main effects for Medication (b = 0.59, SE = 0.61, t(95.0) = 0.98, p = .33) and pain-related disability (b = 0.09, SE = 0.47, t(94.7) = 0.19, p = .85) were not significant. The three-way Med × log pain-related disability × Time interaction was significant for both the early (b = −1.66, SE = 0.75, t(97.6) = −2.22, p = .029) and late (b = 1.77, SE = 0.75, t(95.9) = 2.35, p = .021) phases, suggesting that ibudilast also attenuated the link between pain-related disability and drinks per day observed in the placebo group. Simple slopes analyses showed that pain-related disability did not significantly predict drinks per day in either arm at either phase (all ps > 0.27; 95 % CIs for all slope estimates include zero).

Drinks per drinking day

Pain Intensity.

Drinks per drinking day showed significant phase effects (Time1: b = −3.139, SE = 0.892, t(97.5) = −3.52, p = .001, 95 % CI [−4.888, −1.391]; Time2: b = 2.747, SE = 0.898, t(96.7) = 3.06, p = .003, 95 % CI [0.987, 4.507]), but its interactions with pain and medication were at trend-level only (all p = .07–0.09), indicating a similar pattern to drinks per day but with less precision.

Pain-Related Disability.

The model showed significant main effects for the early phase (Time1: b = −2.71, SE = 0.73, t(100.1) = −3.71, p < .001) and the late phase (Time2: b = 2.38, SE = 0.74, t(99.2) = 3.22, p = .002). Main effects for Medication (b = 1.15, SE = 0.81, t(95.3) = 1.42, p = .16) and pain-related disability (b = 0.17, SE = 0.64, t(94.9) = 0.27, p = .79) were not significant. None of the two-way or three-way interactions were statistically significant (all ps > 0.32).

5. Discussion

The present secondary analysis of a completed 12-week randomized controlled trial (Ray et al., 2025) provides novel evidence that baseline pain intensity moderates the effects of ibudilast on heavy drinking among individuals with AUD. Specifically, ibudilast attenuated the positive association between pain intensity and PHDD observed in the placebo group. Notably, this moderation effect was consistent across both early (baseline–week 2) and later (weeks 4–12) treatment phases and was not attributable to baseline differences in drinking severity, as pain intensity was unrelated to any alcohol use measure at study entry. Interestingly, when we tested baseline pain-related disability as a moderator, it did not significantly moderate the effect of ibudilast on PHDD. By contrast, a more complex pattern emerged for other drinking outcomes: both pain intensity and pain-related disability significantly moderated treatment effects on drinks per day, while no significant moderation was observed for drinks per drinking day or percent days abstinent. This complex pattern where pain intensity moderated PHDD (frequency), but both pain measures moderated drinks per day (quantity) may suggest that different aspects of the pain experience may drive different dimensions of drinking behavior. Our findings support a hypothesis wherein pain intensity may be a primary driver of the decision to engage in a heavy drinking day, while pain-related disability may be more related to the volume of alcohol consumed. It is important to note that while the three-way interactions for drinks per day were significant, the follow-up simple slope analyses were not. This indicates that the significant moderation was driven by the difference between the slopes of the treatment and placebo groups, rather than by a large, significant association within any single group. This overall pattern suggests that ibudilast may help mitigate the risk associated with higher pain intensity (assessed via the Graded Chronic Pain Scale), rather than uniformly lowering drinking across all patients.

These findings extend the parent trial’s primary results, which found no overall effect of ibudilast on drinking outcomes in the full sample, by identifying a clinically meaningful patient subgroup that may benefit from this pharmacotherapy. Pain has long been recognized as a prevalent and impactful factor in AUD (Edwards et al., 2020; Larson et al., 2007; Witkiewitz et al., 2015), and our results suggest that it may also serve as a precision medicine marker for neuroimmune-targeted treatments. The observed specificity to PHDD aligns with evidence that heavy drinking frequency is a robust predictor of relapse risk, medical morbidity, and functional impairment (Witkiewitz et al., 2019), and may reflect ibudilast’s potential to disrupt the reinforcement cycles that sustain high-intensity drinking in the context of pain.

Mechanistically, these results are consistent with preclinical and clinical work linking ibudilast’s actions to pathways implicated in both pain and alcohol use. Pain and AUD share overlapping neurobiological substrates, including neuroinflammation, glial activation, and dysregulated phosphodiesterase signaling (Egli et al., 2012; Elman et al., 2016; Navratilova et al., 2015). Ibudilast’s dual role as a selective PDE inhibitor (PDE3, PDE4, PDE10, and PDE11) and glial modulator allows it to reduce pro-inflammatory cytokine release and enhance neurotrophic signaling, potentially normalizing pain-related alterations in mesocorticolimbic reward circuits. In rodent models, ibudilast attenuates alcohol self-administration and cue-induced reinstatement while reducing markers of central nervous system inflammation (Bell et al., 2015; Fujita et al., 2018). In neuropathic pain models, ibudilast reduces mechanical hypersensitivity and pro-inflammatory cytokine expression (Megat et al., 2022), further supporting its translational relevance to pain and alcohol co-occurrence.

Clinical evidence also supports ibudilast’s potential in pain populations. Johnson, Kwok (Johnson et al., 2015) demonstrated that ibudilast was well-tolerated in patients with chronic neuropathic pain, with preliminary evidence for analgesic benefit. The current findings build upon that work by demonstrating that, in individuals with AUD, higher pain intensity predicts worse heavy drinking outcomes under placebo, but this risk pattern is reduced under ibudilast. This attenuation suggests that ibudilast may disrupt the reciprocal reinforcement between pain and heavy drinking, a cycle in which alcohol is used for acute analgesia but ultimately exacerbates pain and dependence risk.

From a clinical perspective, the high prevalence of pain symptoms in AUD treatment settings underscores the potential impact of targeting this subgroup. Pain is rarely incorporated into AUD pharmacotherapy trials as a stratification or moderation factor, yet our findings suggest that doing so may identify treatment-responsive phenotypes. Moreover, given that PHDD is a clinically meaningful endpoint linked to health outcomes, the present findings could inform both trial design and personalized treatment planning. These results highlight a potential role for ibudilast in “leveling the playing field” for individuals with high pain intensity, who in the absence of treatment may be more vulnerable to heavy drinking.

Several limitations warrant consideration. First, this was a secondary, exploratory analysis of a completed trial not originally powered for moderation testing. While the pain × treatment interactions were statistically significant and theoretically grounded, replication in larger, prospectively designed samples is necessary. Second, pain was measured with the Graded Chronic Pain Scale, a validated instrument that does not capture qualitative pain features (e.g., neuropathic vs. nociceptive), affective distress, or relevant biomarkers. Our finding that pain intensity, but not pain-related disability, moderated the effect on heavy drinking frequency further underscores that different aspects of the pain experience may have distinct relationships with drinking outcomes. Furthermore, the sample on average reported low pain intensity, and the observed moderation effect may be even more pronounced in an AUD sample with clinically significant levels of pain intensity. Third, un-measured confounding (e.g., opioid use, trauma history) could contribute to both pain and treatment response. Finally, the findings may not generalize beyond the relatively healthy outpatient AUD sample included in the parent trial. Future research should prospectively stratify by pain intensity and type when testing neuroimmune modulators for AUD, incorporate repeated pain assessments to evaluate bidirectional effects, and integrate peripheral or central biomarkers of neuroinflammation (e.g., cytokine profiling, TSPO PET imaging) to clarify mechanistic pathways. Testing whether ibudilast or similar agents reduce both drinking and pain symptoms could advance integrated treatment models for this high-risk subgroup.

In summary, this secondary analysis of (Ray et al., 2025) provides initial evidence that pain intensity moderates the effect of ibudilast on heavy drinking in AUD, with ibudilast attenuating the risk associated with higher baseline pain intensity. These results highlight the value of incorporating pain phenotyping into AUD pharmacotherapy trials and suggest that neuroimmune-targeted treatments may be particularly effective in individuals with co-occurring pain and AUD.