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A novel human laboratory alcohol self-administration paradigm for medication screening: Modeling the ability to
Sherry A McKee1, Terril L Verplaetse1
1Yale School of Medicine, Dept of Psychiatry, 2 Church St South, Suite 109, New Haven, CT, United States 06519.
Background:
Human laboratory analogues of drinking behavior provide an efficient, cost-effective mechanistic evaluation of a medication signal on drinking. We developed a novel alcohol self-administration paradigm which models the ability to resist drinking and heavy drinking.
Methods:
We compared a de-escalating schedule of monetary reinforcement (n=16, 50% female) to no schedule (n=16, 50% female) on the ability to resist drinking (i.e., latency to start drinking) and subsequent ad-libitum alcohol consumption of preferred alcoholic beverage in participants with alcohol use disorder (AUD). Participants completed two laboratory sessions designed to model the ability to resist drinking using stress (versus neutral imagery, within-subject factor) as a prime for drinking.
Results:
Participants consumed more alcohol with no schedule (74.2%) versus with the de-escalating reinforcement schedule (40.3%,). The de-escalating schedule reduced alcohol consumption by 49%. Eighty-one percent of participants drank heavily with no schedule and this was reduced with the schedule. Use of the de-escalating schedule also increased the latency to pour and sip the first drink. Participants poured and sipped alcohol faster following stress imagery (vs. neutral), had greater craving, and consumed more alcohol in the first 30 minutes.
Conclusions:
Our novel alcohol self-administration model generated heavy drinking. Over 80% of participants without reinforcement consumed more than 2/3 of their preferred alcoholic beverage designed to increase blood alcohol levels to 0.12 mg% within a 2-hour window. Our model was sensitive to stress, and the de-escalating schedule highlighted stress effects on drinking. Thus, this model is ideal for a cross-over design to test medications for AUD.
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