Memantine misuse and social networks: A content analysis of Internet self-reports

No prescription? No problem: A qualitative study investigating self-medication with novel psychoactive substances (NPS)

BACKGROUND

The proliferation of Novel Psychoactive Substances (NPS) presents a challenge for global drug policy. The ease of online drug purchase and the emergence of the dark web have created new avenues for the growth of NPS. Despite the global nature of this issue, limited research has examined motivations of use. These include perceived safety or convenience, an interest in novel pharmacology and self-exploration. Recent evidence has suggested individuals may be self-medicating with NPS, however this phenomenon has yet to be thoroughly explored. This study aims to investigate the occurrence of NPS self-medication, identify the specific NPS involved, and understand the motivations behind their use.

METHODS

Discussions surrounding self-medication using NPS were collected between October 2022 and February 2023 via a content analysis of a Reddit community. Ninety-three threads, comprising 182,490 words and 5023 comments, were collected and cleaned. A frequency analysis was conducted to identify the NPS discussed, and data was analysed systematically through the process of iterative categorization (IC).

RESULTS

Our study revealed frequent discussions about the self-medication with several NPS, notably etizolam, clonazolam, diclazepam, flualprazolam, 2-FMA, 4F-MPH, 3-FPM and 3-MeO-PCP. Individuals were mainly self-treating ADHD, anxiety and depression. Motivations for choosing NPS included access, cost, legality and a dissatisfaction with conventional healthcare. Substances were often chosen based on a profile of "Functionality" and outcomes varied. The use of clonazolam was highlighted as particularly problematic.

CONCLUSION

The current study provides insight into the phenomenon of self-medication with NPS within an internet demographic, exploring the motivations behind why individuals choose NPS for a variety of disorders. The easy access to NPS and lack of scientific data pose a significant challenge for drug policy. Future policies should focus on improving healthcare providers knowledge of NPS use, removing barriers to adult ADHD diagnosis and rebuilding trust between individuals and addiction services.

Internet search patterns for psychoactive substance use prevention and treatment in Mexico: A cross-sectional study

Objectives

This study was aimed at describing the patterns of searches for information on the prevention and treatment of psychoactive drug use in Mexico, among both the general population and the personnel dedicated to the prevention and treatment of this type of substance use in Mexico.

Methods

An exploratory cross-sectional quantitative study was performed with a validated online questionnaire to collect sociodemographic information, background information and self-reported internet search patterns on psychoactive substance use prevention. A chi-square test was used to identify differences between groups, and a classification tree was used to analyze the search patterns. The combinations of the search criteria with the search topics were entered into Google Trends to validate the information.

Results

The participants (n = 544 adults) were mostly women (65%), 18-30 years of age and bachelor's degree holders (57%). A total of 32% were students, 59% searched the Internet for drug use prevention or treatment, and 12% professionally engaged in drug use prevention or treatment.

Conclusions

Statistically significant differences were found between the general population and professionals dedicated to drug dependency services. We identified six search patterns used in the decision-making process by people seeking information on drug prevention and treatment on the Internet. These patterns were graphically visualized with a classification tree, although, this method did not allow clear differentiation of patterns between groups. The search patterns were successfully validated with Google trends.

Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance

Drug abuse is a serious problem in the United States, with over 90,000 drug overdose deaths nationally in 2020. A key step in combating drug abuse is detecting, monitoring, and characterizing its trends over time and location, also known as pharmacovigilance. While federal reporting systems accomplish this to a degree, they often have high latency and incomplete coverage. Social-media-based pharmacovigilance has zero latency, is easily accessible and unfiltered, and benefits from drug users being willing to share their experiences online pseudo-anonymously. However, unlike highly structured official data sources, social media text is rife with misspellings and slang, making automated analysis difficult. Generative Pretrained Transformer 3 (GPT-3) is a large autoregressive language model specialized for few-shot learning that was trained on text from the entire internet. We demonstrate that GPT-3 can be used to generate slang and common misspellings of terms for drugs of abuse. We repeatedly queried GPT-3 for synonyms of drugs of abuse and filtered the generated terms using automated Google searches and cross-references to known drug names. When generated terms for alprazolam were manually labeled, we found that our method produced 269 synonyms for alprazolam, 221 of which were new discoveries not included in an existing drug lexicon for social media. We repeated this process for 98 drugs of abuse, of which 22 are widely-discussed drugs of abuse, building a lexicon of colloquial drug synonyms that can be used for pharmacovigilance on social media.

Association between NMDAR antagonists, drug abuse and dependence: A disproportionality analysis from the WHO pharmacovigilance database

Ketamine and dextromethorphan are widely abused psychoactive substances. Inhibition of N-methyl-d-aspartate receptors (NMDARs) results in neurobehavioural effects including hallucinations, "out of body" sensations and dissociative effects. However, little is known about a possible extended addictive class effect linked to pharmacologically-related amino-adamantane derivatives (e.g., amantadine and memantine). Using a quasi-Bayesian analytic method, we investigated the potential association between the use of approved NMDAR antagonists (i.e., dextromethorphan, ketamine, amantadine and memantine) and the reporting of drug abuse and dependence in the WHO pharmacovigilance database (VigiBase®), which includes >21 million individual case safety reports collected from >130 countries. This disproportionality analysis identified a significant association for all investigated drugs: dextromethorphan (IC = 3.03 [2.97-3.09]), ketamine (IC = 1.70 [1.57-1.83]), amantadine (IC = 0.21 [0.06-0.35]) and memantine (IC = 0.27 [0.13-0.40]), suggesting a class effect for drug abuse and dependence. This first signal requires further investigations, but health professionals need to be alert to the potential of abuse of NMDAR antagonists, especially in the current "opioid epidemic" context, due to their growing interest as non-opioid antinociceptive drugs.

Application of Real-World Data and the REWARD Framework to Detect Unknown Benefits of Memantine and Identify Potential Disease Targets for New NMDA Receptor Antagonists

BACKGROUND

Observational data may inform novel drug development programs by identifying previously unappreciated, clinical benefits of existing drugs. Several preclinical and clinical studies have suggested emergent therapeutic utility of drugs acting on the N-methyl-D-aspartate (NMDA) receptor, a subtype of glutamate receptors, including the antidementia drug memantine.

METHODS

Using a self-controlled cohort study design, the association of exposure to the NMDA receptor antagonist memantine with the incidence of all observed disease outcomes in four US administrative claims databases, spanning from January 2000 through January 2019, was assessed. The databases used in this study were the IBM MarketScan® Commercial Database (CCAE), the IBM MarketScan® Multi-State Medicaid Database (MDCD), the IBM MarketScan® Medicare Supplemental Database (MDCR), and the Optum© De-Identified Clinformatics® Data Mart Database. Outcomes were defined according to the unique Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) classification system codes and required a diagnosis on two or more distinct dates. Of 20,953 outcomes assessed, only those for which memantine was associated with a ≥ 50% reduction in risk in two or more databases were included. A meta-analysis with random effects was used to pool data across the databases.

RESULTS

Overall, 312,336 patients were exposed to memantine during the study. After removing conditions related to dementia and memory loss, 60 outcomes met the threshold criteria. Results fell into five disease categories: mental disorders, substance use disorders, pain, gastrointestinal and colon disorders, and demyelinating disease. The bulk of findings fell into the first two groups, with 28 outcomes related to mental disorders and 24 related to substance use disorders.

CONCLUSION

The present results confirm that NMDA receptor antagonism may have broader therapeutic utility than previously recognized. Further observational and clinical research may be warranted to explore the therapeutic benefit of NMDA antagonists for the outcomes found in this study.