A practice quit model to test early efficacy of medications for alcohol use disorder in a randomized clinical trial.

Abstract

Screening novel medications for alcohol use disorder (AUD) requires models that are both efficient and ecologically-valid. Ideally, such models would be associated with the outcomes of a given medication in clinical trials. To test a novel human laboratory model in which individuals with intrinsic motivation to change their drinking engage in a "practice quit" attempt consisting of 6 days of complete abstinence from alcohol. Individuals with current AUD completed a randomized, double-blind, placebo-controlled study of naltrexone (50 mg), varenicline (2 mg bid), or matched placebo. Participants were titrated onto the study medication for 1 week prior to starting the 6-day practice quit attempt. During the practice quit attempt, participants completed daily interviews with research staff. All participants completed an alcohol cue-exposure paradigm before starting the study medication and after 2 weeks of study medication. There were no significant medication effect on drinks per drinking day (F(2,49) = 0.66, p = 0.52) or percent days abstinent (F(2,49) = 0.14, p = 0.87) during the 6-day practice quit period. There were no medication effects on alcohol cue-reactivity (F(2,44) = 0.80, p = 0.46). Notably, participants sharply reduced their drinking during the entire 13-day medication treatment period, as compared to reducing only during the 6-day practice quit period. During the total medication period, higher levels of motivation to change was associated with higher percent days abstinent (F(1,49) = 8.12, p < 0.01). This study reports mostly null findings, which challenges us to decompose its nuanced design to consider model refinements. Possible changes to the model include considering the requirement for intrinsic motivation for change, including a longer practice quit period, encompassing the medication administration timeframe in the practice quit period, increasing the required sample size for signal detection, and examining a post COVID-19 pandemic cohort.