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. Author manuscript; available in PMC: 2023 Oct 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2021 May 13;30(5):560–574. doi: 10.1037/pha0000464

Influence of Pregabalin Maintenance on Cannabis Effects and Related Behaviors in Daily Cannabis Users

Joshua A Lile 1,2,3,✉, Joseph L Alcorn III 1, Lon R Hays 3,4, Thomas H Kelly 1,2,3, William W Stoops 1,2,3, Michael J Wesley 1,2,3, Philip M Westgate 5
PMCID: PMC8969895  NIHMSID: NIHMS1787628  PMID: 33983765

Abstract

No medications are approved for cannabis use disorder (CUD), though a small clinical trial demonstrated that the voltage-dependent calcium channel (VDCC) ligand gabapentin reduced cannabis use in treatment seekers. VDCCs are modulated by cannabinoid (CB) ligands, and there are shared effects between CB agonists and VDCC ligands. This overlapping neuropharmacology and the initial clinical results supported the evaluation of pregabalin, a “next-generation” VDCC ligand, as a CUD medication. Two separate placebo-controlled, double-blind, counterbalanced, within-subjects human laboratory studies tested placebo and 300 (N = 2 females, 11 males; Experiment [EXP] 1) or 450 (N = 3 females, 11 males; EXP 2) mg/day pregabalin in cannabis users who were not seeking treatment or trying to reduce/quit their cannabis use. The protocol consisted of two outpatient maintenance phases (11 days in EXP 1 and 15 days in EXP 2) that concluded with four experimental sessions within each phase. During experimental sessions, maintenance continued, and participants completed two 2-day blocks of sampling and self-administration sessions to determine the reinforcing effects of smoked cannabis (0 and 5.9% THC), as well as subjective, attentional bias, performance and physiological responses. In addition, naturalistic cannabis use, side effects, sleep quality, craving, and other self-reported substance use were measured during pregabalin maintenance. Cannabis was self-administered and produced prototypical effects, but pregabalin generally did not impact the effects of cannabis or alter naturalistic use. These human laboratory results in cannabis users not trying to reduce/quit their use do not support the efficacy of pregabalin as a stand-alone pharmacotherapy for CUD.

Keywords: marijuana, self-administration, reinforcement, subjective, attentional bias

Introduction

There is an established yet unmet need for highly effective treatments for cannabis use disorder (CUD). The most recent data from the United States National Survey on Drug Use and Health indicate that 11.5% of Americans aged 12 and older used cannabis in the past month, with 1.8% endorsing criteria for CUD (Center for Behavioral Health Statistics and Quality, 2020). Cannabis was reported as the primary substance for 13% of treatment admissions included in the most recent state agency data systems (Center for Behavioral Health Statistics and Quality, 2017), which is the 3rd most common drug after opioids (34%) and alcohol (29%). Unlike those drugs, no FDA-approved medications are available to manage CUD, so efforts to identify medications for CUD are ongoing (Brezing & Levin, 2018).

To date, over 20 placebo-controlled, randomized clinical trials have been conducted to evaluate medications in treatment-seeking cannabis users for their ability to reduce cannabis use. Recent trials have obtained promising results with oral cannabidiol (Freeman et al., 2020), nabiximols (Sativex®), a cannabis extract-based oromucosal spray containing delta9-tetrahydrocannabinol (THC) and cannabidiol (Lintzeris et al., 2019), and a fatty acid amide hydrolase inhibitor (PF-04457845; D’Souza et al., 2019). However, at the time the present studies were initiated, only a single small pilot trial with gabapentin (Neurontin®) had detected a positive treatment signal in adults (Mason et al., 2012). In that study, 1200 mg per day of gabapentin decreased urinary THC metabolite levels, self-reported cannabis use, craving and depression, and improved performance on tests of executive function. Gabapentin and its analog pregabalin (Lyrica®) are indicated for the treatment of neuropathic pain and seizures, and their therapeutic effects have been linked primarily to voltage-dependent calcium channels (VDCCs), particularly those containing α2δ-1 subunits (Sills, 2006). Although the consequences of ligands binding the α2δ-1 subunit are not completely understood, there is evidence for a direct operational impact on calcium conductance, as well as VDCC trafficking to control the number of functional channels, with the overall result being a dampening of neuronal activity that impacts the release of multiple neurotransmitters (Gale & Houghton, 2011).

Examining gabapentin as a potential CUD medication is logical given the functional link in VDCC and cannabinoid (CB) systems and overlap in the behavioral and pharmacotherapeutic effects of VDCC and CB drugs. For example, one of the main consequences of CB receptor mediated G-protein activation is the inhibition of VDCCs (Howlett et al., 2010). In addition, there is evidence for CB-receptor-independent modulation of VDCCs by CB ligands, through interaction with the plasma membrane lipid bilayer and by direct interaction with a binding site on the ion channel (Lozovaya et al., 2009). Behavioral studies comparing gabapentinoids and CB ligands have consisted mainly of animal models of neuropathic pain, which have generally found that gabapentin and pregabalin are comparable to CB agonists at reducing pain responses (e.g., Caprioli et al., 2012; Gunduz et al., 2011; Hasnie et al., 2007; Luszczki & Florek-Luszcki, 2012; Palmer et al., 2008; Wallace et al., 2007) and produce synergistic effects when combined (Atwal et al., 2019). One clinical study (Bestard & Toth, 2011) directly compared nabilone, a non-selective CB agonist, with gabapentin in an open label study in neuropathic pain patients. Relative to baseline and across drugs at 3- and 6-month intervals, pain, sleep parameters and anxiety were similarly improved by gabapentin and nabilone. Cross-study comparisons have also demonstrated the ability of CB agonists and gabapentinoids to improve outcomes related to neuropathic pain, sleep and anxiety (e.g., de-Paris et al., 2003; Hindmarch et al., 2005; Karst et al., 2010; Nakano et al., 1978; Roth et al., 2012; Ware et al., 2010). Worth noting is that sleep difficulties, anxiety and physical discomfort are commonly observed during cannabis abstinence and often reported as reasons for continued use (Elkashef et al., 2008).

We are aware of only two laboratory studies that tested a gabapentinoid in the context of CUD. In a preclinical study (Aracil-Fernández et al., 2013), mice were treated repeatedly with CP-55,940, a high-efficacy CB agonist, and then changes in brain gene transcription and motor and anxiety-like behavior were recorded during spontaneous withdrawal and treatment with pregabalin. Pregabalin attenuated anatomically specific changes in the expression of certain genes thought to be involved in CUD (i.e., tyrosine hydroxylase in the ventral tegmental area and CB1 receptors in the nucleus accumbens) as well as the motor and anxiety responses induced by cannabinoid agonist cessation. In a human laboratory study, individuals reporting at least weekly cannabis use learned to discriminate 30 mg of oral THC and then the separate and combined effects of acutely administered gabapentin (600 and 1200 mg) and THC (5, 15 and 30 mg) were assessed (Lile et al., 2016). Both doses of gabapentin alone substituted for the THC discriminative stimulus and engendered similar subjective effects, and also shifted the discriminative-stimulus and subjective effects of the lowest THC doses leftward/upward when combined. That gabapentin shared interoceptive effects with THC in cannabis users and also reduced cannabis use in treatment seekers is consistent with an agonist replacement approach to treating drug use disorders.

The primary objective of the present study was to continue the investigation of the pharmacotherapeutic potential of gabapentinoids by using a hybrid human laboratory model (Wesley et al., 2018) to test the ability of pregabalin to impact naturalistic and experimentally controlled cannabis-use behaviors, as well as the response to cannabis. Pregabalin was developed as a “next generation” gabapentinoid and is considered to be a product improvement. Compared to gabapentin, pregabalin has a better pharmacokinetic profile, with a more rapid (1h versus 3–4h) and complete (≥90% versus 60–33%) absorption that follows linear (i.e., first-order) kinetics, yielding a proportional dose-concentration relationship, rather than saturable (i.e., zero-order) kinetics like gabapentin (Bockbrader et al., 2010). The pharmacokinetic advantages of pregabalin over gabapentin appear to translate into an improved pharmacodynamic response. Meta-analyses indicate that pregabalin is more effective for refractory partial epilepsy and chronic post-surgical pain (Clarke et al., 2012; Delahoy et al., 2010), a therapeutic response to pregabalin is observed in some patients for whom gabapentin is not effective (e.g., Saldaña et al., 2012; Stacey et al., 2010; Toth, 2010), and US-based cost-effectiveness analyses found that pregabalin offered cost savings for fibromyalgia patients due to its greater relative effectiveness and was associated with reduced medical expenses and increased workplace productivity in epilepsy patients (Kleinman et al., 2012; Lloyd et al., 2012).

Cannabis self-administration was chosen as the primary outcome, and naturalistic use was chosen as a secondary outcome, because the ability of an intervention to reduce drug intake has historically been the defining characteristic of an effective treatment for substance use disorder (Comer et al. 2008; Haney & Spealman, 2008). A measure of attentional bias to cannabis cues was included because this phenomenon has been linked to CUD and reducing cannabis attentional bias could be a mechanism by which a medication could be effective for treating CUD (O’Neill et al., 2020). Additionally, a subjective effects questionnaire, the Digit-Symbol-Substitution Test (DSST) and cardiovascular indices (heart rate and blood pressure) were included as measures of the interoceptive effects, possible psychomotor performance impairment, and safety profiles, respectively, of cannabis and pregabalin, alone and in combination. Lastly, side effects, sleep quality, cannabis craving, and other self-reported substance use were measured during pregabalin maintenance. We hypothesized that pregabalin would reduce cannabis intake in the laboratory and natural environment, reduce cannabis cue attentional bias, produce mild performance impairment, be safe and well tolerated, produce few, transient side effects, improve sleep outcomes and reduce cannabis craving.

Method

Participants

Adult men and women, aged 18–50, who could speak and read English were recruited from the local community using flyers, social media, research participant registries, outreach activities, paid digital and print advertisements and referrals. Recruitment materials offered research opportunities for users of marijuana and stated that medications would be administered in some studies. Recruitment materials also stated that participants would be compensated for their time, but the amount was not specified. Potential participants were required to report daily or near-daily cannabis use (i.e., at least 25 days per month of use as defined in Budney et al., 2007) and provide a urine sample positive for cannabis use and negative for other recent illicit substance use. Cannabis was illegal in Kentucky at the time these studies were conducted, but participants were informed of the protections in place to maintain the confidentiality of their data. Potential participants completed demographic, drug-use history and medical history questionnaires, as well as medical screens. Individuals with a history of serious physical disease or non-personality psychiatric disorders according to DSM-IV (computerized Structured Clinical Interview for DSM-IV, Psychmanager, Multi-Health Systems Inc., North Tonawanda, NY; EXP 1) or DSM-5 (online SAGE-SR, Telesage Inc., Chapel Hill, NC; EXP 2) criteria, other than disordered use (i.e., abuse and/or dependence for DSM-IV and use disorder for DSM-5) of nicotine and/or cannabis, were excluded from participating. Further exclusion criteria included seeking or currently receiving treatment for drug use, desire to reduce drug use or abstain from use independent of the study procedures, medical screening outcomes outside normal ranges deemed clinically significant by the study physician, history of, or current, angioedema, seizure disorder, use of prescription medications other than hormonal contraceptives or antibiotics, and pregnancy in women. Participants were also excluded for having first-degree family history of either cardiovascular disease that resulted in premature death or seizure disorder. The Medical Institutional Review Board of the University of Kentucky approved the study and the informed consent document. Participants provided informed consent after the procedures and risks were fully explained, and they were informed that pregabalin was being tested as a possible treatment for CUD.

Participants were compensated a maximum of $1691 (EXP 1) or $1941 (EXP 2), with the actual amount received based on the number of maintenance and experimental sessions completed, money choices made on the cannabis-vs-money choice task and urine samples provided that were negative for recent cannabis use, as outlined below. Subjects received compensation by check at the end of each study visit, based on the type of study visit and whether they qualified for abstinence incentives (see below), as well as a completion payment of approximately $500 if they finished the entire study. This payment structure was described to participants during the initial consent process.

Design

These studies used a placebo-controlled, double-blind, pregabalin- and cannabis-dose counterbalanced, within-subjects design. Participants were enrolled as outpatients at the University of Kentucky. Two active doses of pregabalin, 300 and 450 mg/day, were tested in separate groups of participants across two experiments. Experiment (EXP) 1 (0 and 300 mg/day pregabalin) was completed prior to EXP 2 (0 and 450 mg/day pregabalin). In EXP 1, participants attended two practice sessions prior to completing two 11-day pregabalin maintenance phases (150 mg administered twice per day for a total daily dose of 300 mg, or placebo). Each maintenance phase began on a Monday and ended on the Thursday of the following week, with a 10-day inter-phase interval. Each phase consisted of seven maintenance-only days (Monday through the following Sunday) and four experimental sessions (Monday through Thursday). During experimental sessions, maintenance continued, and participants completed two 2-day blocks of sampling and self-administration sessions to determine the reinforcing effects of cannabis (0 and 5.9% THC). In EXP 2, participants received placebo and 450 mg of pregabalin per day (divided into two daily doses of 225 mg), and the maintenance phase lasted 15 days (Thursday through Thursday), with a 6-day inter-phase interval. Table 1a and Table 1b illustrates the design and events for both experiments. Note that these schedules were most typical, though occasionally schedules were offset by 1–2 days, or additional maintenance days were added to accommodate participant schedules and/or avoid sessions on holidays.

Table 1a.

EXP 1 Pregabalin Maintenance (Maint), Sampling, and Self-Administration (Self-Admin) Sessions

First Maintenance Phase (0 mg/day pregabalin) 0% THC 5.9% THC
Study Day 1 2 3 4 5 6 7 8 9 10 11
Week Day M T W Th F S Su M T W Th
Activity Maint Maint Maint Maint Maint Maint Maint Sampling Self-Admin Sampling Self-Admin
Urinalysis ✓ ✓ ✓
10-Day Tapering Dose and Washout Period
Second Maintenance Phase (300 mg/day pregabalin) 5.9% THC 0% THC
Study Day 12 13 14 15 16 17 18 19 20 21 22
Week Day M T W Th F S Su M T W Th
Activity Maint Maint Maint Maint Maint Maint Maint Sampling Self-Admin Sampling Self-Admin
Urinalysis ✓ ✓ ✓
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The order of pregabalin maintenance dose and %THC concentration is representative.

✓ indicates days on which urine samples were collected for semi-quantitative urinalysis.

Table 1b.

EXP 2 Pregabalin Maintenance (Maint), Sampling, and Self-Administration (Self-Admin) Sessions

First Maintenance Phase (0 mg/day pregabalin) 0% THC 5.9% THC
Study Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Week Day Th F S Su M T W Th F S Su M T W Th
Activity Maint Maint Maint Maint Maint Maint Maint Maint Maint Maint Maint Sampling Self-Admin Sampling Self-Admin
Urinalysis ✓ ✓ ✓
6-Day Tapering Dose and Washout Period
Second Maintenance Phase (450 mg/day pregabalin) 5.9% THC 0% THC
Study Day 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Week Day Th F S Su M T W Th F S Su M T W Th
Activity Maint Maint Maint Maint Maint Maint Maint Maint Maint Maint Maint Sampling Self-Admin Sampling Self-Admin
Urinalysis ✓ ✓ ✓
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The order of pregabalin maintenance dose and %THC concentration is representative.

✓ indicates days on which urine samples were collected for semi-quantitative urinalysis.

General Procedures

Participants were required to abstain from illicit drugs other than cannabis throughout participation. Daily urine tests to assess recent drug use and pregnancy were negative throughout. Participants were also asked to avoid any over-the-counter medication, with the exception of non-steroidal anti-inflammatory analgesics.

Maintenance Days

On maintenance-only days, participants completed online questionnaires to assess recent activities, sleep, side effects and drug use, and staff completed an Observer Rating Questionnaire. Participants were administered the first of the two daily pregabalin doses and were given a “take-home” dose. Participants did not report to the laboratory on weekends. On Fridays, participants received additional take-home capsules, and they completed the online questionnaires remotely over the weekend. Maintenance day attendance was reinforced with a $5 USD payment that increased by $5 across each visit. Failure to attend a visit reset the value to $5.

Pregabalin dosing adherence outside of the laboratory (i.e., presumptive dosing) was monitored via Wisepill RT2000 technology (Wisepill Technologies, Somerset West, South Africa), which makes use of mobile phone and Internet technologies to provide real-time medication management. If a participant missed a maintenance dose, s/he would have been permitted to re-start that maintenance condition a single time, but this did not occur. Two participants were discharged during the second maintenance phase due to failure to attend scheduled appointments. One participant in EXP 2 initiated a phase and did not complete it due to a personal issue but was later re-enrolled and completed the protocol; a second participant was lost to follow-up prior to initiating the second maintenance phase.

At the end of each maintenance phase, participants were tapered off of pregabalin. Upon completion of the final experimental session in each phase, participants received envelopes that contained either placebo or descending amounts of pregabalin, labeled with the time and date they should be consumed. Participants also received instructions for taking the tapering doses and emergency contact information.

Abstinence Reinforcement Procedures

To simulate the motivation to quit using cannabis, and in an effort to enhance the ability to detect pregabalin effects on naturalistic cannabis use, participants were asked to attempt abstinence from cannabis use and were incentivized to abstain on maintenance days prior to experimental cannabis administration. In EXP 1, urine samples obtained on study days 1 (phase 1 baseline), 5, 8, 12 (phase 2 baseline), 16 and 19 were collected for semi-quantitative urinalysis; in EXP 2, samples were collected on study days 1 (phase 1 baseline), 5, 12, 16 (phase 2 baseline), 20 and 27. Abstinence was determined by assaying urine samples for the THC metabolite 11-nor-9-carboxy-Δ9-THC (THC-COOH) and creatinine. Analyses were conducted by Quest Diagnostics. Creatinine-normalized THC-COOH ratios less than 100 ng/mL were considered negative for the purpose of determining whether a participant qualified for an incentive, consistent with methods used in clinical trial designs (e.g., Levin et al., 2013). When participants submitted a sample below this threshold, they received $100, for total possible abstinence earnings of $400 per participant. Creatinine-normalized THC-COOH levels, instead of samples coded as positive or negative for use, were analyzed in an effort to enhance sensitivity to the effects of pregabalin on naturalistic cannabis use by employing a continuous instead of a dichotomous outcome variable.

Sessions

Practice and experimental (i.e., sampling and self-administration) sessions were conducted on weekdays and lasted 6.5 h. Participants were asked to refrain from food, caffeine, alcohol, tobacco and cannabis use prior to arrival. At intake, a breath sample was obtained to assess recent alcohol use. Participants also completed a field sobriety test (Toland & Green, 1991) and were observed by trained research staff for cannabis intoxication (e.g., bloodshot, glassy eyes); intoxication was not detected. Participants were required to consume a low-fat snack at intake. Participants who smoked tobacco cigarettes were allowed to smoke a single tobacco cigarette upon arrival to the laboratory to avoid testing under conditions of nicotine withdrawal. They were not allowed to smoke again until the session had ended. The maintenance day questionnaires and dosing procedures described above were also completed during sessions.

Participants were reassessed at the end of the session for possible intoxication and/or impairment using the field sobriety test prior to release. In addition, participants were required to report no drug effects. If necessary, participants were retained at the laboratory beyond the scheduled session time until residual drug effects dissipated. Participants were compensated $50 for completing each session and also received an additional $50 per session if they completed the entire study.

Practice Sessions

The first practice session duplicated a sampling session and the second a self-administration session. Participants smoked un-blinded placebo cannabis cigarettes to become acquainted with the paced smoking procedure (Foltin et al., 1987). On the practice self-administration session, participants were required to work to receive the maximum number of puffs (8) to familiarize them with the response requirements.

Sampling Sessions

In EXP 1, study days 8, 10, 19 and 21 were sampling sessions (i.e., one for each pregabalin and cannabis dose combination), which were conducted to familiarize participants with the effects of the cannabis they could work for in a subsequent session (Stoops et al., 2007). In EXP 2, study days 12, 14, 27 and 29 were sampling sessions. One hour after maintenance dosing, participants received four puffs from two cannabis cigarettes (i.e., 8 total puffs); this delay was imposed to account for the time allowed to complete the self-administration task in the subsequent session and keep the different sessions types on a similar timeline. Participants were instructed to pay attention to the effects of the cannabis, as they would be given the opportunity to work to receive puffs from the same cannabis condition in the next session.

Self-Administration Sessions

Self-administration sessions occurred on study days 9, 11, 20 and 22 in EXP 1 and on study days 13, 15, 28 and 30 in EXP 2. Immediately after maintenance dosing, participants completed a self-administration task to earn puffs of cannabis sampled the day prior and/or an alternative monetary reinforcer ($0.50), which were concurrently available on independent progressive-ratio (PR) schedules. A maximum of 8 reinforcers could be earned (e.g., 8 puffs of cannabis, $4 or some combination of cannabis puffs and money), and participants were required to make a total of 8 choices. The initial response requirement for each reinforcer was 400 responses (i.e., mouse clicks). The completion of a response requirement for a given reinforcer (i.e., cannabis puffs or money) increased the response requirement for that reinforcer by 200. Once the task was complete, participants self-administered the number of puffs earned using the paced smoking procedure. Physiological and behavioral measures were completed as scheduled, regardless of whether cannabis was administered.

Outcome measures

Sessions

In addition to the primary outcome (i.e., self-administered puffs), the following secondary measures were obtained. Because the number of cannabis puffs varied on self-administration sessions, only results from secondary measures obtained on sampling sessions are reported. Data were collected in fixed order, prior to, and following, cannabis administration for 4 h, at regular intervals, which varied by outcome measure (see below).

A 20-item, 100-unit Visual Analog Scale (VAS) Subject-Rated Drug-Effect Questionnaire (Wesley et al., 2018) was administered 1 h prior to, immediately following, and 15, 30, 45, 60, 90, 120, 180 and 240 min after, cannabis administration. Heart rate and blood pressure were also measured at these times. The DSST was administered at the same timepoints and was included as a means of detecting overall performance impairment (Lile et al., 2012). In EXP 2, participants also completed a Street Value Questionnaire at the end of the session.

A visual probe task that incorporated a signal detection component and eye tracking technology was used to measure attentional bias to cannabis cues and attention-based performance. The structure and parameters of this task have been described in detail previously (Alcorn et al., 2019) but summarized here. Each trial consisted of the presentation of an orienting stimulus (750 ms), followed by two side-by-side images (cannabis and/or matched neutral images; 1000 ms), and then one of two visual probe targets (‘X’ or ‘/’, “go” and no-go” targets, respectively) on the right or left side, which remained for 750 ms or until a response was emitted. Participants were instructed to respond to the ‘X’ by pressing a response key that corresponded to the side on which the probe appeared, and to refrain from responding to the ‘/’. Critical trials presented a cannabis and a neutral image and varied systematically according to all possible image and visual probe type and location combinations. A Tobii X2–60 eye tracker (Tobii Technology, Danderyd, Sweden) was used to determine fixation time (ms) and number of fixations on each cue type. Additional dependent variables of interest were mean response times to ‘X’ (i.e., go) targets, d’ (a measure of the ability to discriminate the visual probe targets) and criterion c (a measure of response bias) values.

Maintenance Days

Participants were asked whether they had experienced 45 of the most common and/or serious side effects of pregabalin (Lyrica® Product Information) and cannabis withdrawal (Budney et al., 2003). If a side effect item was endorsed, participants were asked to indicate the severity (mild, moderate or severe) and whether they felt it was due to the study medication. They also reported standard alcohol drinks consumed, tobacco cigarettes smoked, times cannabis was used and puffs taken in the past 24 h. Participants were asked about other drug use, but none was reported, consistent with urine drug screen results. Participants completed the 12-item version of the Marijuana Craving Questionnaire-Short Form (MCQ-SF) (Heishman et al., 2009), and a past 24-h sleep questionnaire to determine minutes to fall asleep, hours asleep, times awake in the night and whether a sleep aid, including cannabis, was used. Lastly, they rated sleep quality, and positive mood and alertness upon awakening (Vandrey et al., 2013).

Observer Rating Questionnaire. This locally developed VAS scale included eight items (the participant appears: anxious, sedated, confused, physically unstable; the participant is having: swelling, difficulty communicating, abnormal/erratic behavior; and overall impairment), scored by a trained research assistant.

Drug Administration

Cannabis cigarettes (0 and 5.9% THC) were provided by the National Institute on Drug Abuse. Pregabalin (0, 150 and 225 mg) was administered in one opaque capsule containing commercially available Lyrica® (Pfizer, Inc., New York, NY) twice per day. Doses were based on those recommended for neuropathic pain and seizures (i.e., 150–600 mg/day). Doses were escalated across the initial maintenance days (3 days in EXP 1, 9 days in EXP 2) up to the target dose, based on the Product Information recommendations. Prior to drug administration, heart rate was assessed. If heart rate exceeded 100 bpm, drug administration would have been withheld, but this did not occur.

Data Analyses

To account for the correlation among repeated measures, generalized estimating equations with random participant effect, or exchangeable correlations, were used to fit a linear model for each outcome. For binary outcomes, in which marginal probabilities were of interest, an identity link was incorporated in the model while utilizing a marginal Bernoulli distribution. Fixed effects for pregabalin and cannabis, as well as their interaction, were used as predictors. Attentional bias analyses included an additional cue type predictor. Session data having multiple timepoints were analyzed as maximum or minimum effects, as appropriate, after cannabis administration. For maintenance day outcomes (i.e., side effects, self-reported cannabis, alcohol and tobacco use in the past 24 h, MCQ-SF and sleep questionnaire), pregabalin was the only predictor. To minimize outliers, the average of the observations from a given participant for a given dose was calculated and used for the linear mixed models. The semi-quantitative urinalysis analysis included pregabalin and time (baseline and 2 samples collected during each maintenance condition) as predictors. For the Observer Rating Scale, the majority of the responses had a zero value, so data were analyzed as the probability of having a value >0. Small-sample corrected standard errors were utilized with between-within degrees of freedom. All tests were two-sided and utilized a 5% significance level. For those outcomes having more than one predictor, significant interactions were followed with post-hoc pair-wise comparisons; otherwise, main effects are reported. Analyses were conducted in SAS version 9.4.

Results

Participant Demographics

Thirteen participants were enrolled in EXP 1 and completed at least one of the two maintenance phases; 11 participants completed both phases. Fourteen participants were enrolled in EXP 2 and completed at least one of the two maintenance phases; 13 participants completed both phases. All participants indicated that smoking was a primary route of cannabis administration. A summary of their demographic characteristics is shown in Table 2.

Table 2.

Participant Demographics

EXP 1 EXP 2
Total N (completed both phases) 13 (11) 14 (13)
Black 2 female, 2 male 1 male
Hispanic/Latino 1 male 1 female
White 8 male 1 female, 9 male
Mixed Race/Ethnicity 0 2 male
Age in years (range, mean) 19–32, 26 18–31, 25
Education years (range, mean) 10–16, 13 10–16, 14
Weight in kg (range, mean) 64–111, 81 48–120, 78
Hormonal birth control 1 1
Past month days of cannabis use (range, mean) 25–31, 30 25–31, 29
DSM IV or 5 cannabis criteria met (range, mean) * 1–6, 3 0–6, 3
Past week alcohol drinks (N, range, mean) 9, 2–14, 7 5, 2–28, 8
Past month heavy drinking days (N, range, mean) ** 7, 1–12, 3 2, 1–6, 4
DSM IV or 5 alcohol criteria met * 0 0
Daily tobacco cigarettes (N, range, mean) 5, 4–20, 12 6, 4–12, 6
Other drug use in month prior to screening *** 0 1
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*

DSM-IV criteria for abuse and dependence in EXP 1, DSM-5 criteria for use disorder in EXP 2.

**

Heavy drinking day defined as ≥4 drinks in women and ≥5 drinks in men.

***

One participant reported 1x hallucinogen and 3x cocaine use in the month prior to screening, but did not test positive for recent cocaine use.

Outcomes

Table 3 shows model estimates and 95% confidence intervals for outcomes from both experiments with significant main effects and/or interactions.

Table 3.

Model estimates and 95% confidence intervals (CI) for outcome measures with a significant main effect or interaction.

EXP 1 Outcomes Effect Estimate (95% CI)
Self-Administration Cannabis 3.8 (1.5, 5.3)
VAS-Any Effect Cannabis 37.8 (19.3, 56.4)
VAS-Good Effect Cannabis 39.7 (20.2, 59.2)
VAS-High Cannabis 45.0 (26.6, 63.4)
VAS-Sedated Cannabis 16.4 (2.1, 30.7)
VAS-Like Drug Cannabis 46.4 (25.7, 67.2)
VAS-Stimulated Cannabis 23.9 (6.9, 40.9)
VAS-Take Again Cannabis 44.1 (21.5, 66.8)
VAS-Pay For Cannabis 43.8 (23.3, 64.2)
VAS-Shaky / Jittery Cannabis 9.1 (0.3, 17.8)
VAS-Hungry Cannabis 24.4 (5.5, 43.3
VAS-Thirsty Cannabis 29.7 (13.9, 45.5)
VAS-Stoned Cannabis 42.5 (25.0, 60.1)
VAS-Forgetful Cannabis 12.5 (0.7, 24.3)
VAS-Difficulty Concentrating Cannabis 15.0 (0.1, 29.8)
Heart Rate Cannabis 13.7 (3.3, 24.1)
Diastolic Pressure Can 5.9% + Pre 300 mg vs. −4.3 (−8.1, −0.4)
Can 5.9% + Pre 0 mg
Can 5.9% + Pre 0 mg vs. 5.6 (0.9, 10.3)
Can 0% + Pre 0 mg
Dot Probe Task Gaze Time Cue 33.5 (6.9, 60.2)
Dot Probe Task Fixations Cue 6.9 (2.8, 10.9)
Dot Probe Task Criterion C Cue −0.1 (−0.2, 0.0)
Normalized urine THC-COOH Time −97.7 (−1766, −18.8)
EXP 2 Outcomes Effect Estimate (95% CI)
VAS-Any Effect Cannabis 18.1 (8.4, 27.7)
VAS-Good Effect Cannabis 18.8 (8.5, 29.0)
VAS-High Cannabis 21.5 (11.1, 32.0)
VAS-Like Drug Cannabis 19.1 (8.5, 30.0)
VAS-Stimulated Cannabis 11.9 (5.2, 18.7)
VAS-Take Again Cannabis 22.7 (12.3, 33.2)
VAS-Pay For Cannabis 19.1 (8.2, 30.0)
VAS-Dizzy Cannabis 2.1 (0.1, 4.1)
VAS-Hungry Cannabis 10.8 (2.0, 20.0)
VAS-Stoned Cannabis 23.3 (12.8, 33.8)
VAS-Suspicious Cannabis 2.2 (0.7, 3.7)
Street Value Questionnaire Cannabis 6.9 (3.9, 10.0)
DSST Number Correct Pregabalin −2.7 (−5.1, −0.2)
DSST Percent Correct Pregabalin −3.4 (6.6, −0.2)
Heart Rate Cannabis 12.6 (3.4, 21.7)
Heart Rate Pregabalin 5.4 (0.5, 10.3)
Dot Probe Task Gaze Time Cue 29.0 (13.2, 44.9)
Dot Probe Task Fixations Cue 5.6 (2.4, 8.8)
Dot Probe Task Fixations Cannabis 7.1 (0.0, 14.2)
Dot Probe Task d’ Pregabalin × Cue 0.16 (0.0, 0.3)
Sleep Latency Pregabalin −1.2 (−2.1, −0.2)
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Cannabis Self-Administration

Participants in EXP 1 self-administered a significantly greater number of puffs of smoked cannabis containing active THC (p < 0.001) compared to placebo, but the number of puffs did not vary by pregabalin condition. In EXP 2, the difference in the number of self-administered puffs of active versus placebo smoked cannabis approached significance (p = 0.06). Self-administration data from both experiments are shown in Figure 1.

Figure 1.

Figure 1.

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Self-administration (number of puffs; Y-axis) of smoked cannabis containing 0% THC (X-axis, left side) and 5.9% THC (X-axis, right side) during 0 mg/day (open bars) and 300 mg/day (filled bars, top panel) or 450 mg/day pregabalin (filled bars, bottom panel). Uni-directional brackets indicate 1 SEM. The asterisk denotes a significant effect (p < 0.001) of cannabis concentration.

Subject Ratings

In EXP 1, significant main effects of cannabis were found for fourteen VAS items: Any Effect, Good Effects, High, Sedated, Stoned, Like Drug, Stimulated, Take Again, Pay For, Shaky/Jittery, Hungry, Difficulty Concentrating, Thirsty and Forgetful (p’s ≤ 0.05). Active cannabis significantly increased ratings on these items relative to placebo, but these ratings were not impacted by 300 mg/day pregabalin. Similarly, in EXP 2, significant main effects of cannabis were found for eleven VAS items: Any Effect, Good Effects, High, Stoned, Like Drug, Stimulated, Take Again, Pay For, Dizzy, Hungry and Suspicious (p’s ≤ 0.05). Active cannabis significantly increased ratings on these items relative to placebo, but these ratings were not impacted by 450 mg/day pregabalin. Results for the questionnaire items Any Effect and Stoned from both experiments are presented in Figure 2.

Figure 2.

Figure 2.

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Peak Visual Analog Scale ratings (out of 100) of Any Effect (Y-axis; left panels) and Stoned (right panels; Y-axis) following administration of smoked cannabis containing 0% THC (X-axis, left side) and 5.9% THC (X-axis, right side) during 0 mg/day (open bars) and 300 mg/day (filled bars, top panels) or 450 mg/day pregabalin (filled bars, bottom panels). Uni-directional brackets indicate 1 SEM. The asterisks denote a significant effect (p’s < 0.001) of cannabis concentration.

A significant main effect of cannabis (p < 0.001) was observed on the Street Value Questionnaire in EXP 2. Values were not affected by pregabalin. When collapsed across pregabalin dose, the value (mean±SEM) attributed to placebo cannabis was $1.31±0.54 and the value of active cannabis was $8.27±1.63.

Performance

In EXP 1, no significant effects of cannabis or pregabalin were detected on the DSST. In EXP 2, the number and percentage of correct responses on the DSST was significantly reduced by 450 mg/day pregabalin (p’s = 0.03 and 0.04, respectively). When collapsed across cannabis conditions, pregabalin decreased the number and percentage of correct responses on the DSST by approximately 2% and 4%, respectively.

Heart Rate, Blood Pressure and Temperature

Active cannabis significantly elevated heart rate in EXP 1 and EXP 2 (p’s < 0.05). When collapsed across pregabalin conditions, peak heart rate was 93.63±4.85 beats per minute (bpm; mean±SEM) following administration of active cannabis and 80.58±2.96 bpm after placebo cannabis in EXP 1, and 84.63±5.26 bpm after active cannabis and 73.11±2.78 bpm after placebo in EXP 2. In addition, heart rate was increased by 450 mg/day pregabalin in EXP 2 (450 mg/day pregabalin = 81.85±4.49 bpm, 0 mg/day pregabalin = 76.11±4.38 bpm, collapsed across cannabis conditions; p = 0.03). In EXP 1, there was an interaction between cannabis and pregabalin on diastolic blood pressure (p = 0.02); post-hoc analysis indicated that 300 mg/day pregabalin + active cannabis reduced diastolic blood pressure relative to active cannabis alone, whereas active cannabis + placebo pregabalin increased diastolic blood pressure relative to placebo pregabalin alone. No effects of cannabis or pregabalin were observed on blood pressure in EXP 2.

Attentional Bias

In EXP 1, main effects of cue type (i.e., cannabis versus neutral), but not cannabis or pregabalin, were detected for gaze time, number of fixations and criterion c on the visual probe task (p’s < 0.05). Participants spent longer fixating on cannabis images compared to matched neutral images, looked at cannabis images more frequently and were biased towards not responding to the visual probe target following neutral cues. In EXP 2, main effects of cue type were detected for gaze time and number of fixations on the visual probe task (p’s = 0.01 and <0.001, respectively). Participants spent longer looking at cannabis images compared to matched neutral images and looked at cannabis images more frequently. A main effect of cannabis was also observed for the number of fixations (p = 0.05), with active cannabis significantly increasing the number of times participants looked at the images. Lastly, there was an interaction between pregabalin dose and cue type found for d’ (p = 0.01), attributed to an increase in d’ when cannabis cues were presented during 450 mg/day pregabalin maintenance, indicating an improvement in the ability to discriminate the visual probe targets under this condition. Gaze time and number of fixations from both experiments are shown in Figure 3.

Figure 3.

Figure 3.

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Gaze time in ms (Y-axis, left panels) and number of fixations (Y-axis, right panels) on the eye-tracking attentional bias task following administration of smoked cannabis containing 0% and 5.9% THC (X-axis) during 0 mg/day (open bars) and 300 mg/day (filled bars, top panels) or 450 mg/day pregabalin (filled bars, bottom panels). Uni-directional brackets indicate 1 SEM. The single asterisk denotes a significant main effect of cannabis concentration (p = 0.05) and the double asterisk denotes a significant effect of cue type (p’s < 0.05).

Naturalistic Cannabis, Alcohol and Tobacco Cigarette Use

Due to canceled sessions (e.g., holidays and weather) or technical issues, some urine samples were not collected or analyzed, and were not included in the analysis. In EXP 1, 3 out of 72 urine samples were omitted; all 3 occurred during placebo maintenance. A significant effect of time (p = 0.01), but not pregabalin, was found; creatinine-normalized levels of urine THC-COOH were significantly reduced at the first but not the second post-baseline assessment, regardless of pregabalin condition (Figure 4). 60% (13 out of 22) post-baseline samples were below the 100 ng/mL THC-COOH threshold during placebo maintenance and 33% (8 out of 24) post-baseline samples were below this threshold during 300 mg/day pregabalin maintenance. During placebo maintenance, 42% (5 out of 12) of participants provided urine samples >100 ng/mL THC-COOH at baseline that dropped below this threshold for the remaining two samples, indicative of abstinence. During 300 mg/day pregabalin maintenance, 17% (2 out of 12) attained and sustained abstinence, according to this operational definition. Self-reported cannabis use (times used and total puffs taken in the past 24 h) and the number of self-reported standard alcohol drinks and tobacco cigarettes smoked were also not impacted by pregabalin. In EXP 2, 8 out of 78 urine samples were omitted; 7 occurred during placebo maintenance. In addition, post-baseline data for the subject who only completed one maintenance phase were either not collected (first post-baseline timepoint) or not able to be analyzed (second post-baseline timepoint) due to staff errors, so data from that subject are omitted. Creatinine-normalized levels of urine THC-COOH were not significantly different as a function of pregabalin dose or time (Figure 4). 19% (4 out of 21) post-baseline samples were below the 100 ng/mL THC-COOH threshold during placebo maintenance and 12% (3 out of 26) post-baseline samples were below this threshold during 450 mg/day pregabalin maintenance. During placebo maintenance, no participants provided urine samples >100 ng/mL THC-COOH at baseline that dropped below this threshold for the remaining two samples, indicative of sustained abstinence. During 450 mg/day pregabalin maintenance, one participant attained and sustained abstinence, according to this operational definition. Self-reported cannabis use (times used and total puffs taken in the past 24 h; Figure 4) and the number of self-reported standard alcohol drinks and tobacco cigarettes smoked were also not impacted by pregabalin (data not shown).

Figure 4.

Figure 4.

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Creatinine-normalized levels of urine THC-COOH as measured by semi-quantitative urinalysis (Y-axis) during maintenance on 0 mg/day (open circles) and 300 mg/day (filled circles, top panels) or 450 mg/day (filled circles, bottom panels). Y-axis abbreviations: BL = Baseline, M = Monday, T = Tuesday, W = Wednesday, Th = Thursday, F = Friday, S = Saturday, Su = Sunday. Uni-directional brackets indicate 1 SEM. The asterisk denotes a significant effect of time (p = 0.01).

Side Effects

In both experiments, there was a zero-incidence rate for all side effects in at least one of the maintenance conditions for each outcome, so statistical analysis could not be conducted. Instead, side effects attributed to study medication that occurred at a rate >5% during a given maintenance condition are noted. In EXP 1, participants reported nausea of mild or moderate severity on 8 occasions during 300 mg/day pregabalin maintenance and 9 occasions during placebo. Participants also reported dry mouth of mild or moderate severity on 7 occasions during 300 mg/day pregabalin maintenance and no occasions during placebo. In EXP 2, participants reported nasal congestion of mild or moderate severity on 12 occasions during 450 mg/day pregabalin maintenance and 2 occasions during placebo. No severe side effects were noted in either experiment.

MCQ-SF

No effects of 300 or 450 mg/day pregabalin were detected on the MCQ-SF.

Sleep

No effects of 300 mg/day pregabalin were detected on sleep outcomes. Maintenance on the 450 mg/day dose significantly reduced the latency to fall asleep (p = 0.02; 15.8±4.0 min versus 14.0±2.2 min).

Observer Rating Questionnaire

In EXP 1, one participant had a non-zero value for the questionnaire item Swelling during placebo maintenance and one participant had a non-zero value for the questionnaire item Difficulty Communicating during active pregabalin; however due to zero counts for these items for the other maintenance condition, a p value could not be calculated. No significant effects of pregabalin maintenance were found for the other items. In EXP 2, all questionnaire items had zero values for all participants, with the exception of a value of 20 out of 100 for the item Anxious in one participant during placebo maintenance; consequently, no analyses could be conducted.

Discussion

The objective of these two studies was to continue the investigation of the pharmacotherapeutic potential of gabapentinoids by using a human laboratory model to test the ability of maintenance on two active doses of pregabalin to impact naturalistic and experimentally controlled cannabis-use behaviors, as well as the response to cannabis. Prototypical cannabis effects were observed during experimental sessions; cannabis was self-administered, and increased ratings on positive subjective effects questionnaire items and heart rate, but pregabalin generally did not modulate these effects and only had small magnitude effects on a few experimental session outcomes. In addition, pregabalin maintenance did not impact naturalistic cannabis use and had minimal effects on outcomes thought to be related to cannabis use (e.g., sleep, cannabis craving).

The ability of a medication to attenuate the reinforcing effects of abused drugs in laboratory studies has been predictive of therapeutic efficacy (Comer et al. 2008; Haney & Spealman, 2008). For example, we previously demonstrated that maintenance on oral extended release (ER) d-amphetamine reduced intravenous and intranasal cocaine self-administration in human laboratory studies that enrolled non-treatment-seeking participants (Lile et al., 2020; Rush et al., 2010), consistent with some clinical trials in patients seeking treatment for cocaine use disorder (reviewed in Tardelli et al., 2020). Similarly, medications that are clinically effective for the treatment of opioid, alcohol and tobacco use disorders have been found to reduce self-administration of those drugs in laboratory studies (e.g., Comer et al., 2005; Davidson et al., 1999; McKee et al., 2012). That pregabalin failed to impact cannabis self-administration suggests that pregabalin is unlikely to be effective as a treatment for CUD by reducing the reinforcing effects of cannabis.

Although active cannabis functioned as a reinforcer in EXP 1, the main effect of cannabis concentration only approached significance for this outcome in EXP 2. Informal comparison of results across studies shows that EXP 2 participants self-administered more placebo cannabis and less active cannabis, suggesting that they were less sensitive to the reinforcing effects of the active cannabis concentration and less capable of discriminating active from placebo cannabis compared to EXP 1 participants. Consistent with this notion, the subjective effects of cannabis were considerably lower in the EXP 2 participants (see Figure 2). A possible explanation for these group differences is that EXP 2 participants were more tolerant to the acute effects of this relatively low active cannabis concentration due to more cannabis use. Although past-month days of cannabis use and number of DSM IV/5 criteria for cannabis dependence/use disorder did not appear to differ across participants in the two experiments, creatinine-normalized levels of urine THC-COOH were approximately 80% higher in the EXP 2 participants (293±98 versus 529±139 ng/mL; Figure 4), demonstrating greater cannabis use in that group. Regardless, pregabalin did not affect the number of active or placebo cannabis choices in either experiment, suggesting that it would not be an effective CUD treatment by decreasing cannabis intake.

The outpatient medication maintenance procedures used here provided the opportunity to assess the influence of pregabalin on naturalistic use as well as self-administration in a controlled laboratory setting. A similarly designed study that evaluated naltrexone in non-treatment-seeking cannabis-using participants demonstrated that reductions in the reinforcing effects of cannabis in the laboratory translated to altered patterns of use in the natural environment (Haney et al., 2015). Pregabalin did not affect cannabis intake in either context in our studies. One difference between these studies is that we provided incentives for cannabis abstinence ($100 per sample below 100 ng/mL THC-COOH) in an effort to provide additional motivation for participants to abstain from cannabis use, potentially enhancing the ability to detect therapeutic effects of pregabalin. Although pregabalin did not reduce naturalistic cannabis use, as measured by self-report and semi-quantitative urinalysis, the incentives decreased creatinine-normalized THC-COOH levels, but only at the first of two timepoints in EXP 1. These findings are inconsistent with prior studies in treatment-seeking patients, which have demonstrated that incentivizing urine samples negative for recent cannabis use enhances the effectiveness of other treatments (Sherman & McRae-Clark, 2016). These discordant results could reflect differences between treatment seekers and non-treatment seekers in the motivation to abstain from cannabis use, which would be difficult to model in human laboratory studies.

Pregabalin also did not significantly affect the subjective effects of cannabis in the present study. In our prior study, 600 and/or 1200 mg of acutely administered gabapentin enhanced drug-appropriate responding on a drug discrimination task and peak ratings on most subjective effects questionnaire items increased by 5, 15 and/or 30 mg oral THC (Lile et al., 2016). We predicted, based on prior human laboratory studies that tested agonist replacement medications (i.e., compounds that shared a mechanism of action and had overlapping subjective and discriminative-stimulus effects with the abused drug) with demonstrated efficacy in clinical trials (Donny et al., 2005; Rush et al., 2009; Sobel et al., 2004), that pregabalin maintenance would attenuate the subjective response to cannabis, rather than enhance it. Prior studies found that that the subjective effects of the abused drug were reduced during maintenance, possibly via tolerance. In our studies, however, pregabalin did not produce an analogous attenuation of the subjective response to cannabis that would have been characteristic of an agonist replacement medication. One possibility is that treatment with a compound having a common direct mechanism of action (i.e., another cannabinoid receptor ligand) would be needed to alter the subjective effects of cannabis and that maintenance on a drug having shared interoceptive effects, which might be the result of downstream receptor actions, is not sufficient.

Another possibility is that the doses of pregabalin were too low to alter the effects of cannabis or change naturalistic cannabis use. The two doses selected for testing in these studies, 300 and 450 mg/day, represent the mid-range of what is FDA-approved for seizures and neuropathic pain (150–600 mg/day), so the highest dose (i.e., 600 mg) could be considered for further testing as a possible CUD medication. However, a review of clinical trials that tested pregabalin for fibromyalgia indicated that 600 mg/day does not provide therapeutic benefit beyond 450 mg/day and is associated with an increase in side effects (Arnold, 2017), which could extend to the use of pregabalin for CUD as well.

In both experiments, participants exhibited an attentional bias to cannabis cues relative to neutral cues, as measured by a visual probe task and eye-tracking technology, which is consistent with prior studies using similar methods in cannabis users (Alcorn et al., 2019; Field et al., 2006). In addition, the number of fixations to visual cues was increased by active cannabis administration in EXP 2 participants, largely driven by the active cannabis + 450 mg pregabalin condition. We are not aware of any previous research that tested the effects of a gabapentinoid on attentional bias, but two prior studies (Chung & Bae, 2020; Morgan et al., 2010) assessed attentional bias to cannabis cues following administration of cannabis in the natural environment. Specific effects of cannabis administration on attentional bias to cannabis cues were not detected in either study. Additional research under controlled conditions is needed to better understand the impact of acute cannabis or medication use on attentional bias to cannabis cues.

A notable limitation to these studies is that the predictive validity of human laboratory self-administration procedures for the treatment of cannabis use disorder has yet to be demonstrated. To date, it appears that only the combination of THC and the ⍺2A adrenergic agonist lofexidine has been tested in both a human laboratory study and a treatment-oriented placebo-controlled clinical trial. The combination of these drugs improved sleep and decreased cannabis withdrawal, craving and relapse in non-treatment-seeking participants in the laboratory (Haney et al., 2008), but did not promote the treatment success target of 3 weeks of abstinence, or reductions in use, compared to placebo in a clinical trial (Levin et al., 2016). Worth mentioning is that a fully powered, controlled clinical trial to evaluate 1200 mg/day gabapentin as a CUD medication in treatment-seeking patients has been completed but not published (NCT00974376). Gabapentin did not increase the percentage of negative urinary drug screens for recent cannabis use at 12 weeks relative to placebo, which is consistent with the results with pregabalin from the present study. As noted above, some promising findings have emerged from recent clinical trials that found reduced cannabis use in patients being treated with cannabidiol, nabiximols and PF-04457845 (D’Souza et al., 2019; Freeman et al., 2020; Lintzeris et al., 2019). Future research should use one or more of these medications to validate existing human laboratory models or to reverse engineer procedures that are predictive of medication success. In addition, there are several medications that have reduced cannabis self-administration in human laboratory studies that should be evaluated in treatment-seeking patients (e.g., naltrexone, nabilone, nabilone + zolpidem, high dose THC; Haney et al., 2013; Haney et al., 2015; Herrmann et al., 2016; Schlienz et al., 2018), which would provide further information about the predictive validity of these procedures.

Other limitations are that the amount of cannabis used by participants during their participation was not controlled, not all participants met criteria for cannabis abuse/dependence (DSM-IV) or use disorder (DSM-5), and some, but not all, female participants used hormonal contraceptives, which could have impacted study results. However, findings from prior human behavioral pharmacology studies that have examined the influence of ovarian sex hormones on the response to psychoactive drugs have been inconsistent (e.g., Terner and de Wit, 2006), so restrictions on hormonal contraceptive use were not implemented. Although all participants reported daily/near-daily cannabis use, not all subjects met diagnostic criteria for cannabis abuse/dependence or use disorder, so these results might not generalize to a population meeting these diagnostic criteria. However, it is possible that if pregabalin had any efficacy at reducing cannabis intake, it would have been more readily detected in individuals who met fewer diagnostic criteria; however, an orderly relationship was not observed between these criteria and the ability of pregabalin to reduce cannabis self-administration. With respect to the lack of experimental control over the use of cannabis during participation, we felt that this limitation was outweighed by the benefit of being able to assess the potential effects of pregabalin on naturalistic cannabis use as well as experimentally controlled cannabis self-administration.

In conclusion, maintenance on 300 and 450 mg/day pregabalin did not alter cannabis self-administration or cannabis use in the natural environment in daily cannabis users who were not seeking treatment or trying to reduce/abstain from cannabis use. In addition, pregabalin had minimal effects on outpatient outcomes thought to be related to continued cannabis use (e.g., sleep, cannabis craving). These human laboratory results do not support the use of pregabalin as a stand-alone pharmacotherapy for CUD, although a controlled clinical trial in treatment-seeking patients who meet criteria for CUD would be needed to fully evaluate the efficacy of pregabalin for this indication.

Public Significance Statement.

In this outpatient human laboratory study, non-treatment-seeking cannabis users were maintained on pregabalin to determine its ability to reduce the reinforcing and other behavioral effects of cannabis in a controlled laboratory setting, as well as naturalistic use. Pregabalin did not alter cannabis use and had minimal effects on the response to cannabis or outcomes thought to be related to continued cannabis use (e.g., sleep, cannabis craving), which does not support the potential efficacy of pregabalin as a stand-alone pharmacotherapy for cannabis use disorder.

Disclosures and Acknowledgments

This research and the preparation of this manuscript were supported by grants awarded to Dr. Joshua Lile (National Institute on Drug Abuse grant R01 DA036550) as well as the University of Kentucky Center for Clinical and Translational Science (National Center for Advancing Translational Sciences grant UL1TR001998). These funding sources had no other role than financial support.

All authors contributed significantly to the manuscript, and have read and approved the final version.

The authors declare no conflict of interest.

We acknowledge the pharmacy services of the University of Kentucky Investigational Drug Service. We also thank the staff of the Laboratory of Human Behavioral Pharmacology for expert technical assistance.

References

  1. Alcorn JL 3rd, Marks KR, Stoops WW, Rush CR, & Lile JA (2019). Attentional bias to cannabis cues in cannabis users but not cocaine users. Addictive Behaviors, 88, 129–136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Aracil-Fernández A, Almela P, & Manzanares J (2013). Pregabalin and topiramate regulate behavioural and brain gene transcription changes induced by spontaneous cannabinoid withdrawal in mice. Addiction Biology, 18, 252–262. [DOI] [PubMed] [Google Scholar]
  3. Arnold MJ (2017). Pregabalin for fibromyalgia pain in adults. American Family Physician, 96, 291–292. [PubMed] [Google Scholar]
  4. Atwal N, Casey SL, Mitchell VA, & Vaughan CW (2019). THC and gabapentin interactions in a mouse neuropathic pain model. Neuropharmacology, 144, 115–121. [DOI] [PubMed] [Google Scholar]
  5. Bestard JA & Toth CC (2011). An open-label comparison of nabilone and gabapentin as adjuvant therapy or monotherapy in the management of neuropathic pain in patients with peripheral neuropathy. Pain Practice, 11, 353–368. [DOI] [PubMed] [Google Scholar]
  6. Bockbrader HN, Wesche D, Miller R, Chapel S, Janiczek N, & Burger P (2010). A comparison of the pharmacokinetics and pharmacodynamics of pregabalin and gabapentin. Clinical Pharmacokinetics, 49, 661–669. [DOI] [PubMed] [Google Scholar]
  7. Brezing CA & Levin FR (2018). Pharmacotherapy for the treatment of cannabis use disorder. Annals of Internal Medicine, 173, 247–248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Budney AJ, Moore BA, Vandrey RG, & Hughes JR (2003). The time course and significance of cannabis withdrawal. Journal of Abnormal Psychology, 112, 393–402. [DOI] [PubMed] [Google Scholar]
  9. Budney AJ, Vandrey RG, Hughes JR, Moore BA, & Bahrenburg B (2007). Oral Δ9-THC suppresses cannabis withdrawal symptoms. Drug and Alcohol Dependence, 86, 22–29. [DOI] [PubMed] [Google Scholar]
  10. Caprioli A, Coccurello R, Rapino C, Di Serio S, Di Tommaso M, Vertechy M, Vacca V, Battista N, Pavone F, Maccarrone M, & Borsini F (2012). The novel reversible fatty acid amide hydrolase inhibitor ST4070 increases endocannabinoid brain levels and counteracts neuropathic pain in different animal models. Journal of Pharmacology and Experimental Therapeutics, 342, 188–195. [DOI] [PubMed] [Google Scholar]
  11. Center for Behavioral Health Statistics and Quality (2017). Treatment episode data set (TEDS): 2005–2015, National Admissions to Substance Abuse Treatment Services. Substance Abuse and Mental Health Services Administration, Rockville, MD. [Google Scholar]
  12. Center for Behavioral Health Statistics and Quality (2020). 2019 National survey on drug use and health: Detailed tables. Substance Abuse and Mental Health Services Administration, Rockville, MD. [Google Scholar]
  13. Chung T & Bae SW, (2020). Mobile assessment of acute effects of marijuana on cognitive functioning in young adults: Observational study. Journal of Medical Internet Research mHealth and uHealth, 8, e16240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Clarke H, Bonin RP, Orser BA, Englesakis M, Wijeysundera DN, & Katz J (2012) The prevention of chronic postsurgical pain using gabapentin and pregabalin: A combined systematic review and meta-analysis. Anesthesia & Analgesia, 115, 428–442. [DOI] [PubMed] [Google Scholar]
  15. Comer SD, Ashworth JB, Foltin RW, Johanson CE, Zacny JP, & Walsh SL (2008). The role of human drug self-administration procedures in the development of medications. Drug and Alcohol Dependence, 96, 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Comer SD, Walker EA, & Collins ED (2005). Buprenorphine/naloxone reduces the reinforcing and subjective effects of heroin in heroin-dependent volunteers. Psychopharmacology, 181, 664–675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Davidson D, Palfai T, Bird C, & Swift R (1999). Effects of naltrexone on alcohol self-administration in heavy drinkers. Alcoholism: Clinical and Experimental Research, 23, 195–203. [PubMed] [Google Scholar]
  18. Delahoy P, Thompson S, & Marschner IC (2010). Pregabalin versus gabapentin in partial epilepsy: A meta-analysis of dose-response relationships. BioMed Central Neurology, 10, 104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. de-Paris F, Sant’Anna MK, Vianna MR, Barichello T, Busnello JV, Kapczinski F, Quevedo J, & Izquierdo I (2003). Effects of gabapentin on anxiety induced by simulated public speaking. Journal of Psychopharmacology, 17, 184–188. [DOI] [PubMed] [Google Scholar]
  20. Terner JM & de Wit H (2006). Menstrual cycle phase and responses to drugs of abuse in humans. Drug and Alcohol Dependence, 84, 1–13. [DOI] [PubMed] [Google Scholar]
  21. Donny EC, Brasser SM, Bigelow GE, Stitzer ML, & Walsh SL (2005). Methadone doses of 100 mg or greater are more effective than lower doses at suppressing heroin self-administration in opioid-dependent volunteers. Addiction, 100, 1496–1509. [DOI] [PubMed] [Google Scholar]
  22. D’Souza D, Cortes-Briones J, Creatura G, Bluez G, Thurnauer H, Deaso E, Bielen K, Surti T, Radhakrishnan R, Gupta A, Gupta S, Cahill J, Sherif MA, Makriyannis A, Morgan PT, Ranganathan M, & Skosnik PD (2019). Efficacy and safety of a fatty acid amide hydrolase inhibitor (PF-04457845) in the treatment of cannabis withdrawal and dependence in men: A double-blind, placebo-controlled, parallel group, phase 2a single-site randomized controlled trial. Lancet Psychiatry, 6, 35–45. [DOI] [PubMed] [Google Scholar]
  23. Elkashef A, Vocci F, Huestis M, Haney M, Budney A, Gruber A, & el-Guebaly N (2008). Marijuana neurobiology and treatment. Substance Abuse, 29, 17–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Field M, Eastwood B, Bradley BP, & Mogg K (2006). Selective processing of cannabis cues in regular cannabis users. Drug and Alcohol Dependence, 85, 75–82. [DOI] [PubMed] [Google Scholar]
  25. Foltin RW, Brady JV, Fischman MW, Emurian CS, & Dominitz J (1987). Effects of smoked marijuana on social interaction in small groups. Drug and Alcohol Dependence, 20, 87–93. [DOI] [PubMed] [Google Scholar]
  26. Freeman TP, Hindocha C, Baio G, Shaban NDC, Thomas EM, Astbury D, Freeman AM, Lees R, Craft S, Morrison PD, Bloomfield MAP, O’Ryan D, Kinghorn J, Morgan CJA, Mofeez A, & Curran HV (2020). Cannabidiol for the treatment of cannabis use disorder: A phase 2a, double-blind, placebo-controlled, randomized, adaptive Bayesian trial. Lancet Psychiatry, 7, 865–874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gale JD & Houghton LA (2011). Alpha 2 Delta (α(2)δ) ligands, gabapentin and pregabalin: What is the evidence for potential use of these ligands in irritable bowel syndrome. Frontiers in Pharmacology, 2, 28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Gunduz O, Karadag HC, & Ulugol A (2011). Synergistic anti-allodynic effects of nociception/orphanin FQ and cannabinoid systems in neuropathic mice. Pharmacology, Biochemistry and Behavior, 99, 540–544. [DOI] [PubMed] [Google Scholar]
  29. Haney M, Cooper ZD, Bedi G, Vosburg SK, Comer SD, & Foltin RW (2013). Nabilone decreases marijuana withdrawal and a laboratory measure of marijuana relapse. Neuropsychopharmacology, 38, 1557–1565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Haney M, Hart CL, Vosburg SK, Comer SD, Reed SC, & Foltin RW (2008). Effects of THC and lofexidine in a human laboratory model of marijuana withdrawal and relapse. Psychopharmacology, 197, 157–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Haney M, Ramesh D, Glass A, Pavlicova M, Bedi G, & Cooper ZD (2015). Naltrexone maintenance decreases cannabis self-administration and subjective effects in daily cannabis smokers. Neuropsychopharmacology, 40, 2489–2498. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Haney M & Spealman R (2008). Controversies in translational research: drug self-administration. Psychopharmacology, 3, 403–419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hasnie FS, Breuer J, Parker S, Wallace V, Blackbeard J, Lever I, Kinchington PR, Dickenson AH, Pheby T, & Rice AS (2007). Further characterization of a rat model of varicella zoster virus-associated pain: Relationship between mechanical hypersensitivity and anxiety-related behavior, and the influence of analgesic drugs. Neuroscience, 144, 1495–1508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Heishman SJ, Evans RJ, Singleton EG, Levin KH, Copersino ML, & Gorelick DA (2009). Reliability and validity of a short form of the Marijuana Craving Questionnaire. Drug and Alcohol Dependence, 102, 35–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Herrmann ES, Cooper ZD, Bedi G, Ramesh D, Reed SC, Comer SD, Foltin RW, & Haney M (2016). Effects of zolpidem alone and in combination with nabilone on cannabis withdrawal and a laboratory model of relapse in cannabis users. Psychopharmacology, 233, 2469–2478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hindmarch I, Dawson J, & Stanley N (2005). A double-blind study in healthy volunteers to assess the effects on sleep of pregabalin compared with alprazolam and placebo. Sleep, 28, 187–193. [DOI] [PubMed] [Google Scholar]
  37. Howlett AC, Blume LC, & Dalton GD (2010). CB(1) cannabinoid receptors and their associated proteins. Current Medicinal Chemistry, 17, 1382–1393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Karst M, Wippermann S, & Ahrens J (2010). Role of cannabinoids in the treatment of pain and (painful) spasticity. Drugs, 70, 2409–2438. [DOI] [PubMed] [Google Scholar]
  39. Kleinman NL, Sadosky A, Seid J, Martin RC, & Labiner DM (2012). Costs, work absence, and adherence in patients with partial onset seizures prescribed gabapentin or pregabalin. Epilepsy Research, 102, 13–22. [DOI] [PubMed] [Google Scholar]
  40. Levin FR, Mariani J, Brooks DJ, Pavlicova M, Nunes EV, Agosti V, Bisaga A, Sullivan MA, & Carpenter KM (2013). A randomized double-blind, placebo controlled trial of venlafaxine-extended release for co-occuring dependence and depressive disorders. Addiction, 108, 1084–1094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Levin FR, Mariani JJ, Pavlicova M, Brooks D, Glass A, Mahony A, Nunes EV, Bisaga A, Dakwar E, Carpenter KM, Sullivan MA, & Choi JC (2016). Dronabinol and lofexidine for cannabis use disorder: A randomized, double-blind, placebo-controlled trial. Drug and Alcohol Dependence, 159, 53–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lile JA, Johnson AR, Banks ML, Hatton KW, Hays LR, Nicholson KL, Poklis JL, Rayapati AO, Rush CR, 2020, Stoops WW, & Negus SS (2020). Pharmacological validation of a translational model of cocaine use disorder: Effects of d-amphetamine maintenance on choice between intravenous cocaine and a nondrug alternative in humans and rhesus monkeys. Experimental and Clinical Psychopharmacology, 28, 169–180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lile JA, Kelly TH, & Hays LR (2012). Separate and combined effects of the GABA reuptake inhibitor tiagabine and Δ9-THC in humans discriminating Δ9-THC. Drug and Alcohol Dependence, 122, 61–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lile JA, Wesley MJ, Kelly TH & Hays LR (2016). Separate and combined effects of gabapentin and Δ9-tetrahydrocannabinol in humans discriminating Δ9-tetrahydrocannabinol. Behavioural Pharmacology, 27, 215–224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lintzeris N, Bhardwaj A, Mills L, Dunlop A, Copeland J, McGregor I, Bruno R, Gugusheff J, Phung N, Montebello M, Chan T, Kirby A, Hall M, Jefferies M, Luksza J, Shanahan M, Kevin R, & Allsop D, Agonist Replacement for Cannabis Dependence study group. (2019). Nabiximols for the treatment of cannabis dependence: A randomized clinical trial. Journal of the American Medical Association Internal Medicine, 179, 1242–1253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lloyd A, Boomershine CS, Choy EH, Chandran A, & Zlateva G (2012). The cost-effectiveness of pregabalin in the treatment of fibromyalgia: US perspective. Journal of Medical Economics, 15, 481–492. [DOI] [PubMed] [Google Scholar]
  47. Lozovaya N, Min R, Tsintsadze V, & Burnashev N (2009). Dual modulation of CNS voltage-gated calcium channels by cannabinoids: Focus on CB1 receptor-independent effects. Cell Calcium, 46, 154–162. [DOI] [PubMed] [Google Scholar]
  48. Luszczki JJ & Florek-Łuszczki M (2012). Synergistic interaction of pregabalin with the synthetic cannabinoid WIN 55,212–2 mesylate in the hot-plate test in mice: An isobolographic analysis. Pharmacological Reports, 64, 723–732. [DOI] [PubMed] [Google Scholar]
  49. Mason BJ, Crean R, Goodell V, Light JM, Quello S, Shadan F, Buffkins K, Kyle M, Adusumali M, Begovic A, & Rao S (2012). A proof-of-concept randomized controlled study of gabapentin: Effects on cannabis use, withdrawal and executive function deficits in cannabis-dependent adults. Neuropsychopharmacology, 37, 1689–1698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. McKee SA, Weinberger AH, Shi J, Tetrault J, & Coppola S (2012). Developing and validating a human laboratory model to screen medications for smoking cessation. Nicotine and Tobacco Research, 14, 1362–1371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Morgan CJ, Freeman TP, Schafer GL, & Curran HV (2010). Cannabidiol attenuates the appetitive effects of delta 9-tetrahydrocannabinol in humans smoking their chosen cannabis. Neuropsychopharmacology, 35, 1879–1885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Nakano S, Gillespie HK, & Hollister LE (1978). A model for evaluation of antianxiety drugs with the use of experimentally induced stress: Comparison of nabilone and diazepam. Clinical Pharmacology and Therapeutics, 23, 54–62. [DOI] [PubMed] [Google Scholar]
  53. O’Neill A, Bachi B, Bhattacharyya S (2020). Attentional bias towards cannabis cues in cannabis users: A systematic review and meta-analysis. Drug and Alcohol Dependence, 206, 107719. [DOI] [PubMed] [Google Scholar]
  54. Palmer JA, Higuera ES, Chang L, & Chaplan SR (2008). Fatty acid amide hydrolase inhibition enhances the anti-allodynic actions of endocannabinoids in a model of acute pain adapted for the mouse. Neuroscience 154, 1554–1561. [DOI] [PubMed] [Google Scholar]
  55. Roth T, Lankford DA, Bhadra P, Whalen E, & Resnick EM (2012). Effect of pregabalin on sleep in patients with fibromyalgia and sleep maintenance disturbance: A randomized, placebo-controlled, 2-way crossover polysomnography study. Arthritis Care Research, 64, 597–606. [DOI] [PubMed] [Google Scholar]
  56. Rush CR, Stoops WW, & Hays LR (2009). Cocaine effects during d-amphetamine maintenance: A human laboratory analysis of safety, tolerability and efficacy. Drug and Alcohol Dependence, 99, 261–271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Rush CR, Stoops WW, Sevak RJ, & Hays LR (2010). Cocaine choice in humans during d-amphetamine maintenance. Journal of Clinical Psychopharmacology, 30, 152–159. [DOI] [PubMed] [Google Scholar]
  58. Saldaña MT, Pérez C, Navarro A, Masramón X, & Rejas J (2012). Pain alleviation and patient-reported health outcomes following switching to pregabalin in individuals with gabapentin-refractory neuropathic pain in routine medical practice. Clinical Drug Investigation, 32, 401–412. [DOI] [PubMed] [Google Scholar]
  59. Schlienz NJ, Lee DC, Stitzer ML, & Vandrey R (2018). The effect of high-dose dronabinol (oral THC) maintenance on cannabis self-administration. Drug and Alcohol Dependence, 187, 254–260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sherman BJ & McRae-Clark AL (2016). Treatment of cannabis use disorder: Current science and future outlook. Pharmacotherapy, 36, 511–535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Sills GJ (2006). The mechanisms of action of gabapentin and pregabalin. Current Opinions in Pharmacology, 6, 108–113. [DOI] [PubMed] [Google Scholar]
  62. Sobel BF, Sigmon SC, & Griffiths RR (2004). Transdermal nicotine maintenance attenuates the subjective and reinforcing effects of intravenous nicotine, but not cocaine or caffeine, in cigarette-smoking stimulant abusers. Neuropsychopharmacology, 29, 991–1003. [DOI] [PubMed] [Google Scholar]
  63. Stacey BR, Emir B, Petersel D & Murphy K (2010). Pregabalin in treatment-refractory fibromyalgia. Open Rheumatology Journal, 4, 35–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Stoops WW, Lile JA, Robbins CG, Martin CA, Rush CR, & Kelly TH (2007). The reinforcing, subject-rated, performance, and cardiovascular effects of d-amphetamine: Influence of sensation-seeking status. Addictive Behaviors, 6, 1177–1188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Tardelli VS, Bisaga A, Arcadepani FB, Gerra G, Levin FR, & Fidalgo TM (2020). Prescription psychostimulants for the treatment of stimulant use disorder: A systematic review and meta-analysis. Psychopharmacology, 237, 2233–2255. [DOI] [PubMed] [Google Scholar]
  66. Toland SL & Green W (1991). DRE field testing of drug impaired drivers. In: Watts V, (Ed.), The Effects of Drugs on Human Performance and Behavior: Drugs and Driving/Drugs in the Workplace. American Academy of Forensic Sciences. [Google Scholar]
  67. Toth C (2010). Substitution of gabapentin therapy with pregabalin therapy in neuropathic pain due to peripheral neuropathy. Pain Medicine, 11, 456–465. [DOI] [PubMed] [Google Scholar]
  68. Vandrey R, Stitzer ML, Mintzer MZ, Huestis MA, Murray JA, & Lee D (2013). The dose effects of short-term dronabinol (oral THC) maintenance in daily cannabis users. Drug and Alcohol Dependence, 128, 64–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Wallace VCJ, Segerdahl AR, Lambert DM, Vandevoorde S, Blackbeard J, Pheby T, Hasnie F, & Rice ASC (2007). The effect of the palmitoylethanolamide analogue, palmitoylallylamide (L-29) on pain behaviour in rodent models of neuropathy. British Journal of Pharmacology, 151, 1117–1128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Ware MA, Fitzcharles MA, Joseph L, & Shir Y (2010). The effects of nabilone on sleep in fibromyalgia: Results of a randomized controlled trial. Anesthesia & Analgesia, 110, 604–610. [DOI] [PubMed] [Google Scholar]
  71. Wesley MJ, Westgate PM, Stoops WW, Kelly TH, Hays LR, & Lile JA (2018). Influence of tiagabine maintenance on cannabis effects and related behaviors in daily cannabis users. Experimental and Clinical Psychopharmacology, 26, 310–319. [DOI] [PMC free article] [PubMed] [Google Scholar]

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