It has also been shown that cannabinoid antagonists can prevent drug reinstatement with cocaine, alcohol, and nicotine. Thus, it seems that the future of cannabinoid antagonists in substance abuse treatment is particularly promising, especially in the clinical setting, where polydrug abuse is exceedingly more common than isolated single-drug abuse.The available data suggest that CB1 antagonism produces relatively mild side effects in people. Yet several potential risks were discussed and three in particular received a great deal of attention. First, the possibility of neuropsychiatric sequelae, such as anhedonia and anxiety: preclinical studies have consistently shown such effects in animals, though they have not yet been observed in the clinic. Second, pain and hyperalgesia, because of the pervasive role played by the endocannabinoid system in the control of pain processing. Last, hypertension, as indicated by the contribution of the endocannabinoids to blood pressure regulation and the pressor effects of rimonabant in animal models of hypertension.The endocannabinoid signaling system differs from classical neurotransmitter systems, picking up where classical neurotransmitters leave off. That is, the activation of receptors initiates a series of chemical events that leads to the release of endocannabinoids from the postsynaptic spine e the final step of which is the enzymatic production and subsequent release of anandamide and/or 2-AG. Once released,vertical growing racks cost the endocannabinoids are then directed to the presynaptic cell and the CB1 receptor responds by inhibiting further release of that cell’s neurotransmitters.
The termination of this cascade is accomplished via a transporter that internalizes the endocannabinoids, after which intracellular enzymes such as fatty-acid amide hydrolase break them down. There is a general consensus that endocannabinoids are transported into cells via a facilitated diffusion mechanism. This process may differ both kinetically and pharmacologically from cell to cell. In brain neurons, endocannabinoid transport is blocked by certain agents, which include the compounds AM404, OMDM-8 and AM1172 . However, the pharmacological properties of these drugs in vivo are only partially understood. Once inside cells, endocannabinoids are hydrolyzed by three principal enzyme systems. FAAH is a key enzyme of anandamide deactivation in the brain. Potent and selective FAAH inhibitors have been developed and shown to exert profound antianxiety and antihypertensive effects in animals. The latter effects were discussed at length at the workshop, highlighting the important role of anandamide in two important examples of vascular allostasis e shock and hypertension. In addition to FAAH, another amide hydrolase has been recently characterized, which may participate in the degradation of anandamide and other fatty-acid ethanolamides such as oleoylethanolamine . This amidase prefers acid pH values and has a different tissue distribution than FAAH, being notably high in lung, spleen and inflammatory cells. Inhibitors of this enzyme are being developed. Finally, 2-AG is hydrolyzed by an enzymatic system separate from FAAH, which probably involves a monoacylglycerol lipase recently cloned from the rat brain. Inhibitors of this enzyme are currently under development.What are the therapeutic advantages and drawbacks of using a direct agonist vs. an indirect agonist? Several parallels can be drawn to the well-known SSRIs , which have shown such powerful and useful therapeutic applications in effecting indirect agonism of the serotonergic system.
Indeed, there is ample evidence that pharmacological profiles for the indirectly-acting agonists can generally be attributed to enhanced selectivity based on more localized action. A prime reason for favoring the indirect agonism approach is the possibility of obtaining new drugs devoid of the psychoactive effects and perceived abuse potential of directly acting CB1 agonists. If we accept the postulate of on-demand modulation of endocannabinoid signaling as contributing to some disease states, we are likely to witness the development of more specific medications acting indirectly such as inhibitors of cannabinoid uptake or breakdown.College students often drink alcohol and use drugs simultaneously during parties and other social events . Dual marijuana and alcohol use is especially prevalent, with 47% of marijuana users reporting simultaneous use of alcohol . Furthermore, individuals who have a cannabis use disorder are at increased likelihood for the development of an alcohol use disorder , and rates of substance use disorders and treatment admissions are highest among individuals that use marijuana or alcohol compared to other substances . Approximately 68% of individuals with current CUD and over 86% of those with a history of CUD meet criteria for an AUD . Cannabis dependence doubles the risk for long-term persistent alcohol consequences and dual marijuana and alcohol users consume higher levels of alcohol and experience more alcohol-related consequences than only drinkers . Despite these additional risks, 60% of college students do not perceive regular marijuana use to be harmful .The combination of low perceived risk, policy changes surrounding marijuana legalization, and the rise in marijuana use over the past 10 years heightens the importance of effective interventions for alcohol and marijuana use. In the adult substance use treatment literature, it is relatively well-established that alcohol use negatively impacts treatment of other substances .
In contrast, literature examining the impact of marijuana use on the treatment of other substances is mixed. With the exception of a few studies that do not show marijuana use to negatively influence alcohol or smoking cessation outcomes , many studies have demonstrated that using marijuana before or during alcohol treatment is associated with higher levels of drinking at follow-up . For example, among alcohol dependent individuals, those who used marijuana during alcohol treatment reported fewer days abstinent from alcohol one year following treatment than those who did not use marijuana . Thus, marijuana use seems to have a negative impact on alcohol treatment outcomes. A number of studies have also examined secondary changes in marijuana use following receipt of an alcohol-specific intervention. A recent integrative data analysis study indicated that alcohol BMIs may not facilitate changes in marijuana use among college students ; instead, regardless of treatment condition, college students who successfully reduced their drinking at short- and long-term follow-ups were more likely to be non-users of marijuana or reduce their marijuana use at follow-up. This complementary relationship between marijuana and alcohol use is also supported by research indicating that the risk factors for initiation and maintenance of problematic use are similar across substances . Together, these studies suggest that interventions for alcohol may lead to secondary changes in marijuana use. Consistent with this hypothesis, young adults who participated in an in-person BMIs for alcohol use in an emergency department setting reported greater decreases in marijuana use at the 6-month follow-up than those who received feedback only . Similarly,vertical indoor growing system weekly marijuana users who were seeking treatment for cigarette smoking and completed a brief alcohol intervention within the context of the smoking cessation intervention, demonstrated reductions not only in heavy drinking and tobacco smoking but also in marijuana use . In the college setting, BMIs that target multiple substances have also been associated with reductions in poly-drug use . One explanation for the differential influence of alcohol interventions on marijuana use across these studies may be related to the populations examined. Thus far, alcohol interventions delivered to acute-risk populations have had an impact on marijuana use outcomes, while collectively, interventions delivered to ‘college students’ have not. However, college students are a heterogeneous population, and not all require the same level of intervention .
To our knowledge, no one has examined the influence of an alcohol intervention on marijuana use when alcohol interventions are provided sequentially in the context of stepped care, in which individuals who do not respond to an initial, low-intensity level of treatment are provided a more intensive treatment . The purpose of the current study was to examine marijuana use in the context of a stepped care intervention for alcohol use.We conducted a secondary analysis of data from a randomized clinical trial implementing stepped care with mandated college students . In this study, all participants received a brief advice session administered by a peer counselor. Participants who continued to drink in a risky manner six weeks following the BA session were randomly assigned to either BMI or AO conditions . Step 2 participants who completed the BMI as opposed to AO reported greater reductions in alcohol-related consequences at all follow-up assessments . We tested three hypotheses to examine whether interventions that reduce alcohol-related outcomes may also reduce marijuana use. First, because dual marijuana and alcohol users consume higher levels of alcohol use and experience more alcohol-related consequences , we hypothesized that marijuana users would report higher HED frequency, peak blood alcohol content , and alcohol related consequences in the 6 weeks following a BA session, after controlling for their pre-BA drinking behavior. Second, we hypothesized that heavy-drinking marijuana users who did not respond to the BA session and, therefore, were randomized to a Step 2 BMI or AO would report worse alcohol-related outcomes at 3-, 6-, and 9-month follow-ups than non-users. Third, we examined whether marijuana users changed their marijuana use frequency at any of the three assessment time points following the Step 2 BMI. Examination of marijuana use in this context will improve our understanding of whether marijuana use lessens the efficacy of alcohol interventions, even when delivered sequentially in stepped care. Furthermore, it will inform future intervention efforts aimed at reducing both alcohol and marijuana use.Participants indicated how many times they used marijuana in the past 30 days at baseline and at each follow-up assessment time point. Because marijuana use was highly zero-inflated , and due to our interest in whether being a marijuana user influenced intervention outcomes, dichotomous variables were created to group individuals into user versus non-user for use in analyses to compare these subgroups.To determine if participants who completed Step 1 of the intervention would also complete Step 2, participants reported the number of times they engaged in heavy episodic drinking , defined as consumption of 5+ drinks for males , in the past month. The maximum number of drinks consumed during their highest drinking event in the past month and the amount of time spent drinking during this episode were used to calculate the students’ estimated peak blood alcohol concentration using the Matthews and Miller equation and an average metabolism rate of 0.017 g/dL per hour.Alcohol-related consequences were assessed using the Brief Young Adult Alcohol Consequences Questionnaire , a 24-item subset of the 48-item Young Adult Alcohol Consequences Questionnaire . Dichotomous items are summed for a total number of consequences experienced in the past month. The B-YAACQ is reliable and sensitive to changes in alcohol use over time and has demonstrated high internal consistency in research with college students . In this study, the B-YYACQ demonstrated good internal consistency at baseline, 6-week and follow-up assessments .First, distributions of outcome variables were examined, and outliers falling three standard deviations above the mean were recoded to the highest non-outlying value plus one , resolving initial non-normality in outcomes. Demographic information and descriptive statistics for the outcome variables were calculated . To examine marijuana users’ drinking behavior following BA for alcohol misuse , multiple regression models were run to predict each alcohol outcome variable at the 6- week assessment from baseline marijuana user status , controlling for gender and the corresponding alcohol outcome assessed at baseline. To test hypotheses 2 and 3, hierarchical linear models were run in the HLM 7.01 program , using full maximum likelihood estimation. HLM is ideal for data nested within participants across time, for testing between-person effects and within-person effects on outcomes. An additional advantage of HLM is its flexibility in handling missing data at the within-person level, allowing us to retain for analysis any participant that contributed at least one follow-up assessment. We interpreted models that relied on robust standard errors in the determination of effect significance. All intercepts and slopes were specified as random in order to account for individual variation in both mean levels of the outcomes and time-varying associations. Fully unconditional HLM models were run first in order to determine intraclass correlations for each outcome. ICCs provided information on the percentage of variation in each outcome at both the between- and within-person level. Next, three dummy coded time components were created for inclusion at Level 1.