Background-We have developed an animal model of alcohol self-administration that initially employs schedule-induced polydipsia (SIP) to establish reliable ethanol consumption under open access (22 h/d) conditions with food and water concurrently available. SIP is an adjunctive behavior that is generated by constraining access to an important commodity (e.g., flavored food). The induction schedule and ethanol polydipsia generated under these conditions affords the opportunity to investigate the development of drinking typologies that lead to chronic, excessive alcohol consumption.

Background The current criteria for alcohol use disorders (AUD) do not include consumption (quantity/frequency) measures of alcohol intake, in part due to the difficulty of these measures in humans. Animal models of ethanol self-administration have been fundamental in advancing our understanding of the neurobiological basis of (AUD) and can address quantity/frequency measures with accurate measurements over prolonged periods of time. The non-human primate (NHP) model of voluntary oral alcohol self-administration has documented both binge drinking and drinking to dependence and can be used to test the stability of consumption measures over time. Methods and Results Here, an extensive set of alcohol intakes (g/kg/day) was analyzed from a large multi-cohort population of Rhesus (Macaca mulatta) monkeys (n=31). Daily ethanol intake was uniformly distributed over chronic (12 months) access for all animals. Underlying this distribution of intakes were subpopulations of monkeys that exhibited distinctive clustering of drinking patterns, allowing us to categorically define very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD). These categories were stable across the 12-month assessed by the protocol, but exhibited fluctuations when examined at shorter intervals. Conclusions The establishment of persistent drinking categories based on quantity/frequency suggests that consumption variables can be used to track long-term changes in behavioral, molecular or physiochemical mechanisms related to our understanding of diagnosis, prevention, intervention and treatment efficacies.

BackgroundThe Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well‐documented nonhuman primate (NHP) alcohol self‐administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment.MethodsThe classification strategy uses a machine‐learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self‐administration.ResultsPredictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, “LD and BD” and “HD and VHD.” A subsequent 2‐step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4‐category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings.ConclusionsWe demonstrate that data derived from the induction phase of this ethanol self‐administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having increasingly fewer bouts with more drinks. This analysis also identifies characteristic avoidance of intoxicating topographies in future low drinkers, such as increasing number of bouts and waiting longer before the first ethanol drink.