Social Media as Pharmacovigilance: The Potential for Patient Reports to Inform Clinical Research on Glucagon-Like Peptide 1 (GLP-1) Receptor Agonists for Substance Use Disorders

Abstract

The surge in popularity of semaglutide (Ozempic, Wegovy, Rybelsus) and other glucagon-like-peptide 1 (GLP-1) receptor agonists has been accompanied by widespread reports of unintended reductions in alcohol use (and other addictive behaviors) during treatment. With clinical trials of GLP-1 receptor agonists for substance use only recently under way, anecdotal reports (including via social media) are now a primary reason for interest in potential effects of GLP-1 receptor agonists on alcohol use in patient populations. The nature and volume of these reports raises the prospect that social media data can potentially be leveraged to inform the study of novel addiction treatments and the prioritization of behavioral or neurobiological targets for mechanistic research. This approach, which aligns with recent efforts to apply social media data to pharmacovigilance, may be particularly relevant for drug repurposing efforts. This possibility is illustrated by a thematic analysis of anonymous online reports concerning changes in alcohol use or alcohol-related effects during treatment with GLP-1 receptor agonists. These reports not only support the rationale for clinical trials but also point to potential neurobehavioral mechanisms (e.g., satiety, craving/preoccupation, aversion, altered subjective response) that might inform hypotheses for human laboratory and neuroscience studies. Refined methods for capturing patient reports of incidental medication effects on addictive behaviors at large scale could potentially lead to novel, pharmacovigilance-based approaches to identify candidate therapies for drug repurposing efforts. (J. Stud. Alcohol Drugs, 85, 5–11, 2024)

There remains a pressing need to identify new medications to treat alcohol use disorder (AUD). Seven decades after the U.S. Food and Drug Administration (FDA) approval of disulfiram (the first medication for AUD, approved in 1951), the field has seen only three additional FDA approvals for AUD therapies: acamprosate, oral naltrexone, and injectable naltrexone (approved in 1989, 1994, and 2006, respectively). These treatments are widely available and effective for some. However, AUD medications remain vastly underused, and the pace of medication development has not been adequate to address the broad impact of alcohol-related harms, which account for an estimated 5% of global disease burden and more than 3 million lives lost annually (Witkiewitz et al., 2019; World Health Organization, 2018). Moreover, heterogeneity in clinical presentation and treatment responses requires identification of multiple medication options that can, ideally, be tailored to patient profiles (Litten et al., 2015).

The protracted pace of AUD pharmacotherapy development is attributed to several factors, including enormous time and resource requirements that often make drug development challenging in the absence of industry support (Litten et al., 2016, 2020; Ray et al., 2018). For these reasons, drug repurposing (i.e., identifying existing medications that might prove efficacious for AUD) is an efficient and compelling complement to the endeavor of developing new drugs (Litten et al., 2016; Morley et al., 2021). Importantly, drug repurposing approaches have already yielded notable successes in the field of addiction treatment. Bupropion was evaluated and approved for the indication of smoking cessation after initially being approved as a depression treatment (Ferry & Johnston, 2003). Naltrexone was studied and ultimately approved as a treatment for AUD following its initial approval as an opioid use disorder treatment (Heilig et al., 2011). Therefore, repurposing efforts have already yielded substantial and sustained impact in the treatment of nicotine addiction and AUD. Studies have also generated support for potential off-label use of other drugs to treat AUD, including topiramate and gabapentin (Morley et al., 2021; Witkiewitz et al., 2019). Unfortunately, both FDA-approved and off-label medications for AUD remain “under the radar” in terms of patient, clinician, and public awareness, resulting in underuse and limiting patients’ access to evidence-based treatments. Additional efforts are needed to increase the pace of drug development in the context of AUD.

Pharmacovigilance is the practice of monitoring drug safety, typically through postmarket surveillance using centralized patient or provider reporting systems, such as the U.S. FDA's Adverse Event Reporting System, and analogous systems in countries worldwide (Fornasier et al., 2018). This practice is particularly important to identify risks or side effects that might go undetected in Phase II or Phase III clinical trials, including low-incidence adverse effects or outcomes not prospectively measured in clinical trials. Postmarketing pharmacovigilance is also important to survey side effects among groups underrepresented in clinical trials (including diverse subgroups or subpopulations with co-occurring disorders), and to monitor outcomes in the case of rare diseases. Systematic pharmacovigilance efforts were spurred in response to the well-known case of thalidomide, a drug that was prescribed to pregnant mothers in the 1950s to relieve morning sickness. Roughly a decade later, surveillance revealed that the drug could cause fetal birth defects, a serious event that went undetected in clinical trials but emerged through spontaneous patient reporting (Fornasier et al., 2018). This incident led to the establishment of VigiBase, the World Health Organization pharmacovigilance database.

With the proliferation of social media as a ubiquitous and instantaneous source of information, one possibility is that social media might facilitate timely access to novel findings about unexpected or understudied medication side effects. A scoping review of studies using social media data for pharmacovigilance concluded that side effects reported on social media are largely comparable to those from established reporting systems, with the advantage of faster detection and improved detection of less frequently reported adverse events (Tricco et al., 2018). For example, a study comparing social media reports of methylphenidate use to reports from VigiBase found an 88.5% overlap in adverse event signal detection between the two sources (Chen et al., 2018). Furthermore, researchers detected events from social media posts that were not reported in VigiBase, including certain side effects (e.g., muscle spasms) and incidents of potential drug misuse (e.g., parental procurement of nonprescribed methylphenidate for children).

Whereas postmarketing pharmacovigilance has traditionally emphasized detection of unexpected side effects or adverse events, another potential application is the identification of unexpected therapeutic effects, including effects on clinical outcomes outside of the drug's original indication (van Hunsel & Ekhart, 2021). Postmarketing observations of unexpected therapeutic effects are not unprecedented. As noted, following FDA approval of bupropion for depression (in 1985), clinician observations suggested unintended reductions in cigarette smoking, paving the way for the evaluation and approval of bupropion as a smoking-cessation therapy (Ferry & Johnston, 2003). The potential applications of social media for pharmacovigilance raise the question of whether large-scale data sources could be leveraged to identify unexpected therapeutic effects, including instances in which approved drugs reduce alcohol or other drug use. Such an approach might complement other strategies that have identified drugs for potential repurposing, including qualitative clinician reports and hypothesis-driven pharmacoepidemiology studies (Farokhnia et al., 2022).

Recent events coinciding with expanded clinical use of semaglutide (Ozempic, Wegovy, Rybelsus) may support the notion that patient-driven, opportunistic data (including social media data) could play a role in informing drug repurposing efforts for AUD. Semaglutide, a glucagon-like-peptide 1 (GLP-1) receptor agonist, received FDA approval for the treatment of diabetes in 2017 (Ozempic). The subsequent approval of semaglutide in an oral formulation (Rybelsus) and for the indication of weight control (Wegovy) followed in 2019 and 2021, respectively. The approval of Wegovy, in particular, led to substantial public and media attention to semaglutide's potent weight loss properties. By early 2023, headlines began to emerge noting potential anti-addiction effects of semaglutide, including reported reductions in drinking and loss of desire for alcohol (Blum, 2023). Since then, numerous media outlets have reported on the prospect of semaglutide as a possible AUD therapy, also noting potential implications for other addictive and compulsive behaviors (Zhang, 2023). Absent controlled clinical data on this topic, public commentary has been informed predominantly by anecdotal and unsolicited patient observations, including those accumulating on social media platforms.

Importantly, the idea of investigating GLP-1 receptor agonists as candidate addiction therapies is not entirely new. Preclinical research spanning the past decade has pointed to potential efficacy of GLP-1 receptor agonists for reducing drug motivation, reward, and intake (e.g., Aranäs et al., 2023; Chuong et al., 2023; Egecioglu et al., 2013; Jerlhag, 2020; Shirazi et al., 2013). Following early findings, scientists called for clinical trials of GLP-1 receptor agonists for AUD (Fink-Jensen & Vilsbøll, 2016; Jerlhag, 2018; Shirazi et al., 2013). These observations were followed by the first randomized controlled trial of a GLP-1 receptor agonist in AUD patients (Klausen et al., 2022), which evaluated exenatide (an older GLP-1 receptor agonist, approved by the FDA in 2005). Randomized trials are now underway to assess potential efficacy of GLP-1 receptor agonists for substance use disorders, including several trials investigating semaglutide in participants with AUD. However, with these studies only recently underway, lay commentary about the potential efficacy of GLP-1 receptor agonists for addiction has been driven almost exclusively by anecdotal reports, including those circulating in social media forums. These events have led to the unusual (perhaps unprecedented) situation in which online/social media commentary about a candidate addiction treatment has quickly and drastically outpaced generation of any clinical data (Leggio et al., 2023). This situation is likely to persist for some time while clinical trials are completed.

Caution should be urged in interpreting these anecdotal reports, particularly given the lack of controlled clinical trials on semaglutide in those with substance use disorders (Leggio et al., 2023). At the same time, patient reports are a crucial source of information and can be considered a frontline evidence source that will ultimately contribute to “triangulation” of evidence from various types of research designs (Munafò & Davey Smith, 2018). Moreover, given substantial anecdotal data available in relation to unexpected effects of semaglutide on alcohol use, addiction scientists should consider the possibility that patient-driven social media data could help to inform priorities for clinical and mechanistic research on the potential therapeutic efficacy of GLP-1 receptor agonists for substance use disorders. This scenario could potentially inform future efforts to leverage social media data to support pharmacovigilance-related objectives in the domain of substance use disorder treatment.

To illustrate this point, we compiled and coded data from the social media website Reddit, culling user comments from the three largest Reddit forums related to popular GLP-1 receptor agonists. Given the limitations to relying on opportunistic data from a single online source, we do not present these findings as an empirical study or a systematic analysis. Rather, we present the findings as a case example of the potential for social media data to inform questions concerning therapeutic efficacy and mechanisms in the context of drug repurposing. Comments were culled from the three most popular Reddit forums on this topic: r/Semaglutide, r/Mounjaro, and r/Ozempic, each of which had more than 20,000 users at the time of data collection (similarly sized forums specific to Wegovy were not available at the time of data collection). Posts from these forums with the keyword “alcohol” were scraped using a custom python script that accessed Reddit's application programming interface (API). All comments (i.e., user responses to posts) from alcohol-related posts were also included, yielding a total of 6,367 results. Data were manually filtered to retain those comments referencing firsthand experiences of alcohol use while using one of these medications (1,557 results), ostensibly for the indication of diabetes or weight control. The data set (see the online-only supplemental material) spanned the time of the first post on June 12, 2021, to March 9, 2023 (when the data were scraped). This window reflects a crucial period in which prescription rates for semaglutide were increasing rapidly (Tichy et al., 2023), but before a spike in media reports relating semaglutide to reductions in drinking (in late spring/early summer of 2023). Presumably, patient reports from this period are less likely to be influenced by expectations of beneficial effects on alcohol reduction based on media portrayals.

We conducted a thematic analysis of these reports, taking a hybrid inductive/deductive approach in which primary/common themes were developed based on observations in the qualitative data, with the classification scheme also informed by clinically relevant phenomena (e.g., subjective responses to alcohol, craving). Both authors came to a consensus on the selection and coding of the primary themes. Next, all posts/comments were coded in accordance with whether content reflected each theme. We conducted initial coding sessions to calibrate assignments and used 10% of the data to establish inter-rater reliability (Cohen's κ = .84). Discrepancies were resolved through discussion. The remainder of posts/comments were coded by one author or the other, and frequency counts were compiled (1,503 posts/comments could be assigned to a code; a minority of comments could not be assigned a code because of nonspecific content). Posts/comments could receive more than one code if applicable (i.e., if representing more than one theme), resulting in 942 comments/posts (62.7%) with more than one theme assignment. This process yielded several commonly observed themes, depicted descriptively in Table 1. In addition, because many comments could be assigned more than one theme, exploratory correlations were conducted to examine which themes tended to co-occur at the comment level (Figure 1).

Table 1.

Thematic categories from alcohol-related posts in Reddit forums

Variable	Count (total = 1,503)	Frequency
Lost interest in alcohol	428	28.5%
Craving reduction	157	10.4%
Consumption patterns
Reduced drinking quantity	466	31.0%
Initiated abstinence	163	10.8%
Slower drinking rate	57	3.8%
No change in alcohol use	50	3.3%
Drinking more	10	0.7%
Satiety
Alcohol satiety (satisfied without drinking, or drinking less)	154	10.2%
Physical/gastrointestinal	64	4.3%
Subjective response
Aversive responses while drinking	396	26.3%
Reduction in hedonic/rewarding effects	101	6.7%
Altered taste	99	6.6%
Increased sensitivity to alcohol effects	91	6.1%
Decreased sensitivity to alcohol effects	50	3.3%
Improved control over consumption	19	1.3%
Residual/next-day effects
Headache/hangover	86	5.7%
Residual nausea	57	3.8%
Other	47	3.1%
Participant characteristics
Heavy drinker	211	14.0%
Light drinker	102	6.8%
Other
No change in alcohol effects	176	11.7%
Missing drinking	47	3.1%
Skipping medication dose to facilitate drinking	10	0.7%
Sought medication to decrease drinking	3	0.2%

Figure 1.

Correlation matrix of co-occurring themes from alcohol-related posts in Reddit forums. The phi coefficients (r_φ) derived from Pearson's bivariate correlations are reported. Red shading indicates positive correlations; blue indicates negative correlations. Themes with fewer than 50 occurrences were not included. Subthemes with low occurrence rates (e.g., residual effects) were combined.

Across all user comments, the most prevalent themes were related to self-reported reduced drinking quantity and loss of interest/desire to drink. Many users described a complete or near-complete loss of interest in alcohol. About 10% of reports were coded as indicating that the user had become abstinent. Reports of no perceived change in drinking quantity were also observed, but on a less frequent basis (3%). Reduction of cravings or urges to drink were frequently reported. Some users also reported altered/aversive taste of alcohol drinks.

Changes in the subjective responses and sensitivity to alcohol were also frequently reported, including both increased sensitivity to alcohol's effects (e.g., stronger intoxicating effects, sometimes interpreted as diminished tolerance) and reduced sensitivity to alcohol's effects (typically reported as reductions in the hedonic/rewarding effect of alcohol). Posts also provided an indication of satiety as a possible reason for reductions in alcohol use. Because these comments tended to reflect one of two satiety-related themes, they were coded as reflecting (a) alcohol satiety (e.g., feeling satisfied with fewer drinks, or satisfied without drinking at all) or (b) physical satiety (e.g., feelings of stomach fullness). New onset of aversive responses to alcohol was also frequently reported. Aversive effects were commonly reported as gastrointestinal distress (e.g., nausea, vomiting), with some reports also noting disgust or repulsion from alcohol or the thought of drinking. Residual side effects after drinking (e.g., experiencing side effects the day after drinking) were also frequently reported.

Correlation analyses (r_φ) indicated some significant positive associations and inverse associations among themes (Figure 1). Comments related to alcohol satiety frequently co-occurred with reported reductions in drinking and slower rates of drinking. Reports of reductions in hedonic/rewarding effects and change in the taste of alcohol frequently co-occurred. Reports of alcohol-related satiety correlated positively with reports of reduced drinking and negatively with reports of aversive effects. Because many users referenced their own drinker status (e.g., by self-identifying as heavy or light drinkers or using other terminology), comments were also coded as originating from a heavy or light drinker in cases where users self-identified as such. Overall, 14% of comments were coded as coming from heavy drinkers and 6.8% from light drinkers. Compared with comments that did not endorse heavy drinker status, heavy drinker comments were more likely to include references to craving reduction, reduced drinking quantity, and abstinence (r_φ = .14–.17).

Although this summary of patient reports reflects an opportunistic qualitative analysis rather than an empirical study, the reports serve as a case illustration that pharmacovigilance using large qualitative data sets (including those derived from social media) could potentially facilitate early detection of drug-related changes in substance use patterns, perhaps anticipating questions for further study in controlled clinical trials. Whereas this example used a targeted qualitative approach, analysis of larger data sets could ultimately be aided by the use of passive data collection methods and approaches such as natural language processing and machine learning (Tricco et al., 2018). If such approaches can be refined, potential applications may include the development of data monitoring methods to facilitate identification of approved therapies to prioritize in drug repurposing studies. This possibility is speculative but is consistent with recent efforts to incorporate social media in pharmacovigilance studies (Tricco et al., 2018). Whether the recent move away from open API by some social media companies, such as Reddit and X (formerly known as Twitter), hinders the use of social media for pharmacovigilance remains to be determined (Davidson et al., 2023).

The current example also illustrates that opportunistic patient reports can potentially aid in hypothesis generation for mechanistic studies, a possibility relevant for emergent addiction research on GLP-1 receptor agonists. In this example, unsolicited patient reports implicate several possible therapeutic mechanisms that could inform mechanistic questions for human laboratory or human neuroscience studies of GLP-1 receptor agonists in the context of AUD. These reports appear to implicate behavioral and subjective phenomena that align with known mediators of pharmacotherapy effects (e.g., subjective response, craving), as well as potentially novel mechanisms (satiety). It should be noted that some of these mechanisms could be anticipated based on earlier preclinical studies. For instance, GLP-1 receptor agonists can inhibit alcohol conditioned place preference and dopamine release in the nucleus accumbens in response to alcohol (Egecioglu et al., 2013), and GLP-1 manipulations can produce conditioned taste aversion to sucrose (Thiele et al., 1997). Some of these reports also align with neuroscience studies of GLP-1 in the context of feeding behaviors. Importantly, these reports provide early contextual information for understanding patients’ own perceptions of medication effects on alcohol-related outcomes. Although several consistent themes were identified, heterogeneity of medication effects on alcohol-related outcomes was also apparent, supporting the need to examine individual differences in the effects of GLP-1 receptor agonists in clinical studies.

The qualitative/opportunistic reports examined here are clearly limited in several ways, including the use of single and anonymous data source, the absence of systematic information on patient characteristics or treatment-related factors (e.g., medication dosage or duration), the subjective nature of reports, and other limitations attendant to online data capture methods. Still, this summary provides an early aggregation of firsthand reports of changes in alcohol-related outcomes during treatment with GLP-1 receptor agonists, and at a time when other sources of clinical evidence are limited.

Could this unique instance of unintended therapeutic effects reported at large scale (and predating clinical research findings) be instructive to the field of addiction science? Perhaps one implication is that this sequence of events signals the potential for social media data to inform questions related to unintended medication effects on addictive behaviors. However, further work is required to investigate the utility of this approach, particularly for instances in which medications are not subject to extensive media attention. Importantly, the identification of unexpected therapeutic effects on addictive behaviors requires the assessment of those behaviors in clinical and therapeutic monitoring contexts—a caveat that may explain why similar findings did not emerge from numerous large-scale trials of semaglutide and other GLP-1 receptor agonists for diabetes and obesity. Whether pharmacovigilance occurs in clinical, population, or other settings, timely and opportunistic identification of off-label therapeutic effects on substance use will likely require proactive efforts to systematically incorporate substance use assessments in clinical trial and postmarketing surveillance contexts.