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. Author manuscript; available in PMC: 2014 Dec 15.
Published in final edited form as: Drug Alcohol Depend. 2013 Sep 14;133(3):852–856. doi: 10.1016/j.drugalcdep.2013.09.003

Effects of tolcapone on working memory and brain activity in abstinent smokers: A proof-of-concept study

Rebecca L Ashare a,*, E Paul Wileyto a,b, Kosha Ruparel c, Patricia M Goelz a, Ryan D Hopson c, Jeffrey N Valdez c, Ruben C Gur c, James Loughead c, Caryn Lerman a
PMCID: PMC3960598  NIHMSID: NIHMS525163  PMID: 24095246

Abstract

Background

Dopamine levels in the prefrontal cortex (PFC) are thought to play an important role in cognitive function and nicotine dependence. The catechol-O-methyltransferase (COMT) inhibitor tolcapone, an FDA-approved treatment for Parkinson’s disease, increases prefrontal dopamine levels, with cognitive benefits that may vary by COMT genotype. We tested whether tolcapone alters working memory-related brain activity and performance in abstinent smokers.

Methods

In this double-blind crossover study, 20 smokers completed 8 days of treatment with tolcapone and placebo. In both medication periods, smokers completed blood oxygen level-dependent (BOLD) fMRI scans while performing a working memory N-back task after 24 h of abstinence. Smokers were genotyped prospectively for the COMT val158met polymorphism for exploratory analysis.

Results

Compared to placebo, tolcapone modestly improved accuracy (p = 0.017) and enhanced suppression of activation in the ventromedial prefrontal cortex (vmPFC) (p = 0.002). There were no effects of medication in other a priori regions of interest (dorsolateral PFC, dorsal cingulate/medial prefrontal cortex, or posterior cingulate cortex). Exploratory analyses suggested that tolcapone led to a decrease in BOLD signal in several regions among smokers with val/val genotypes, but increased or remained unchanged among met allele carriers. Tolcapone did not attenuate craving, mood, or withdrawal symptoms compared to placebo.

Conclusions

Data from this proof-of-concept study do not provide strong support for further evaluation of COMT inhibitors as smoking cessation aids.

Keywords: Smoking, Nicotine, COMT, Tolcapone, fMRI, Working memory

1. Introduction

Cognitive deficits are commonly reported during nicotine withdrawal (Hendricks et al., 2006; Hughes, 2007) and have been associated with smoking relapse (Patterson et al., 2010). Dopamine levels in prefrontal cortex (PFC), regulated in part by the catechol-O-methyltransferase (COMT) enzyme, play a role in cognitive function and nicotine dependence (Goldberg and Weinberger, 2004; Nestler, 2005). The functional val158met (rs#4680) polymorphism in the COMT gene results in increased enzyme activity and decreased PFC dopamine for the val allele (Chen et al., 2004; Lachman et al., 1996; Lotta et al., 1995). Evidence suggests that smokers with val/val genotypes exhibit greater abstinence-induced decrements in working memory-related PFC activity and in performance compared with met allele carriers (Loughead et al., 2009). The COMT inhibitor tolcapone, an FDA-approved treatment for Parkinson’s disease, improves working memory and processing efficiency in PFC among healthy controls (Apud et al., 2007); however, some data suggest that cognitive benefits of tolcapone may be specific to persons with val/val genotypes (Apud et al., 2007; Farrell et al., 2012; Giakoumaki et al., 2008). Lastly, COMT genotype has been associated with quitting success in smokers; however, studies to date have produced mixed results (Colilla et al., 2005; Munafo et al., 2008; Omidvar et al., 2009).

This proof-of-concept functional magnetic resonance imaging (fMRI) study used a within-subject cross-over design to examine the effects of short-term treatment with tolcapone vs. placebo on working memory and brain activity following 24 h of abstinence. Based on prior work (Falcone et al., 2013; Loughead et al., 2009), we hypothesized that tolcapone (vs. placebo) would improve working memory and increase BOLD signal change in task-positive regions (bilateral dorsolateral PFC (DLPFC) and medial frontal/cingulate gyrus (MF/CG)), and increase suppression of BOLD signal in task-negative regions (posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC)) during smoking abstinence. All smokers were genotyped prior to study initiation for the COMT val158met polymorphism to explore whether tolcapone effects are more pronounced among smokers with val/val genotypes.

2. Methods

2.1. Participants

Eligible smokers between 18 and 65 years old who smoked at least 10 cigarettes/day for at least 6 months were recruited through mass media. Smokers were prospectively screened based on genotyping for the COMT val158met polymorphism and we attempted to achieve balanced groups with val/val vs. val/met or met/met genotypes [rs4680, Assay on Demand (c_25746809_50) from Applied Biosystems Inc. (Foster City)]. COMT genotypes were in Hardy–Weinberg equilibrium (p = 0.67). Exclusion criteria included: currently seeking treatment for smoking cessation; history of DSM-IV Axis I disorders (except nicotine dependence); use of psychotropic or smoking cessation medications; pregnancy; history of brain injury; left-handedness; presence of fMRI contraindicated material in the body; low or borderline intelligence (<90 score on Shipley’s IQ test); kidney and/or liver disease; use of medications contraindicated for use with tolcapone; and any impairment that would prevent cognitive task performance. Twenty-eight participants completed the study and eight were excluded for poor fMRI data quality (n = 5) and low task accuracy (2SD below the mean (n = 2); more than 30% non-responses (n = 1)).

2.2. Procedures

All procedures were approved by the University of Pennsylvania Institutional Review Board and all participants provided written informed consent (clinicaltrials.gov NCT01001520). This study included two BOLD fMRI sessions following 24 h of abstinence (treatment order counterbalanced). Participants completed a physical examination including blood work, a urine drug screen, breath alcohol test, and pregnancy test. Psychiatric or substance abuse disorders were assessed using the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998). The Shipley Institute of Living Scale (Zachary, 2000) and Fagerström Test for Nicotine Dependence (Heatherton et al., 1991) were also administered.

Tolcapone and matching placebo were provided in blinded blister packs. Tolcapone was administered according to standard guidelines: day 1 (100 mg t.i.d.), days 2–8 (200 mg t.i.d.), day 9 (200 mg b.i.d.), day 10 (200 mg q.d.), and day 11 (100 mg q.d.). A dose-tapering regimen was utilized on days 9–11 to reduce adverse effects associated with discontinuation of tolcapone (Apud et al., 2007).

During each medication period, participants completed one monitoring visit (day 5) and a scanning session (day 8). On scanning session days, blood samples were tested for liver function and those with a positive drug screen, a breath alcohol test > 0.01, or a breath carbon monoxide (CO) test > 9 ppm were excluded. Participants completed measures of withdrawal (MNWS; Hughes et al., 1984), craving (QSU-Brief; Cox et al., 2001), mood (PANAS; Watson et al., 1988), and side effects. Following a practice session to familiarize participants with the task, participants were escorted to the radiology clinic for the fMRI scan. After a 2-week washout period during which participants returned to their baseline smoking level, the second medication period began and followed procedures as described above.

2.3. Task design

The N-back task presents complex geometric figures (fractals) for 500 ms, followed by an interstimulus interval of 2500 ms under four conditions: 0-back, 1-back, 2-back and 3-back. In the 0-back condition, participants respond to a specific fractal; for the 1, 2, and 3-back conditions, participants respond if the current fractal was identical to the one “n” before it. No response was required for nontargets. Each condition was presented three times in 20-trial blocks (25% targets; 60 s). After the first set was presented blocks in order of increasing memory load, conditions were presented pseudo-randomly; visual instructions (9 s) preceded each block. The first 24 s of a 48 s baseline rest period (fixation point) was discarded to ensure the MRI signal reached steady state. To minimize practice effects, equivalent N-back tasks with unique stimuli and presentation sequence were used for the two sessions (order counterbalanced).

2.4. Image acquisition

Data were acquired on a 3T Siemens TIM TRIO scanner (Erlangen, Germany). A T1-weighted whole-brain structural image was acquired for registration to a standard brain atlas (MPRAGE, TR = 1810 ms, TE = 3.51 ms, TI = 1100 ms, FOV = 240 mm × 180 mm, matrix = 256 × 192, resolution = 0.9 mm × 0.9 mm, slices = 160, flip angle = 9°, effective voxel resolution of 1 mm × 1 mm × 1 mm). BOLD fMRI images were acquired using a whole-brain, single-shot gradient-echo (GE) echoplanar sequence (TR/TE = 2000/30 ms, FOV = 240 mm, matrix = 64 × 64, flip angle = 90°, slices = 33, slice thickness/gap = 3.0 mm/0 mm and effective voxel resolution = 3.4 × 3.4 × 3.4).

2.5. Image preprocessing

BOLD fMRI time series data were preprocessed and analyzed using standard procedures using fMRI Expert Analysis Tool (FEAT version 5.98) of FSL (FMRIB’s Software Library, Oxford, UK). Single subject preprocessing included nonbrain removal using BET (Smith, 2002), slice time correction, motion correction to the median image using MCFLIRT (Jenkinson et al., 2002), high pass temporal filtering (100 s), spatial smoothing using a Gaussian kernel (6 mm full-width at half-maximum, isotropic) and mean-based intensity normalization of all volumes using the same multiplicative factor. The median functional volume was coregistered to the anatomical T1-weighted structural volume and then transformed into standard anatomical space (T1 MNI template) using FLIRT (Jenkinson et al., 2002; Jenkinson and Smith, 2001). Transformation parameters were later applied to all statistical contrast maps for group-level analyses.

2.6. Image quality assessment

All images were carefully examined for artifacts, acquisition problems and preprocessing errors. Image quality assessment procedures assessed temporal signal-to-noise ratio (tSNR) of both smoking and abstinence sessions for poor quality data. To assess excessive head motion, mean relative volume-to-volume displacement for each session was also evaluated. Subjects with mean tSNR < 26 (equivalent to 2SD below the mean) and/or mean relative motion > 0.3 were excluded from the analysis. Based on the above criteria, 5 subjects were excluded.

2.7. Subject time-series analysis

Subject-level statistical analyses were carried out voxelwise using FILM (FMRIB’s Improved General Linear Model) with local autocorrelation correction (Woolrich et al., 2001). Four condition events (0-back, 1-back, 2-back, and 3-back) were modeled using a canonical hemodynamic response function. The instruction period (Jenkinson et al., 2002; Smith, 2002) and six motion correction parameters were included as nuisance covariates and the three rest periods (fixation point) were treated as the baseline. Image analysis was completed for each individual in subject space, and resulting contrast maps were spatially normalized as described above.

2.8. Region of interest (ROI) image analysis

To characterize the group (val/val vs. met/met) by treatment (placebo, tolcapone) effects, mean percent signal change was extracted from a priori regions of interest (ROIs) in task-positive (right and left DLPFC and MF/CG) and task-negative regions (vmPFC and PCC). ROI masks were functionally defined from an independent sample (n = 63) studied with the identical N-back task under comparable abstinence conditions (Falcone et al., 2013). ROI masks were then registered into native subject space using methods described above. Finally, mean percent signal change was calculated per subject for the four load conditions separately for each ROI. These values were exported for further analysis using standard statistical software and procedures described below.

2.9. Data analysis

BOLD signal change was examined using random effects maximum likelihood regression (Stata Corporation, College Station, TX, USA). Models included terms for the main effects of treatment (tolcapone vs. placebo), back level (0, 1, 2, and 3), treatment order, and relevant covariates (genotype, race, age, sex, Shipley IQ score, and FTND score). Because interactions of treatment with back level were not significant it was included as a covariate. Behavioral performance (accuracy and reaction time) was tested as described above. Exploratory models tested the genotype × treatment interaction. Because the five a priori ROIs are highly correlated (r ≈ 0.80), alpha for BOLD models was adjusted to p = 0.014 (Sankoh et al., 1997). Alpha remained 0.05 for the performance models. Correlations between BOLD signal and behavioral performance were examined using Pearson’s correlations, separately by treatment period. Finally, we tested for practice effects on working memory performance and BOLD signal change by conducting models comparing the first to the second treatment period, irrespective of treatment condition.

3. Results

3.1. Descriptive data

Table 1 contains smoking and demographic characteristics for the full sample. There were no genotype differences on any variable. CO levels were consistent with the abstinence requirement during placebo (mean = 3.1 ppm, SD = 1.4) and tolcapone (mean = 3.7 ppm, SD = 2.1).

Table 1.

Demographic and smoking characteristics by genotype.

Measure Full sample Val/val (n = 9) Val/met (n = 11) p-Value
Sex, n (%) female 10 (50%) 4 (44%) 6 (55%) 0.65
Race, n (%) Caucasian 15 (75%) 5 (56%) 10 (91%) 0.07
Age 34 (11.6) 38.4 (13) 30.4 (10) 0.13
Nicotine dependence 4.3 (1.6) 4.6 (1.3) 4.1 (1.9) 0.55
Cigarettes per day 15.1 (4.0) 14.4 (4.2) 15.6 (3.8) 0.53
Number of years smoked 16.8 (13.2) 20.9 (15.2) 13.8 (11.4) 0.26
Shipley Institute of Living Scale 108.1 (9.2) 104.3 (7) 111.2 (10) 0.10
Order of medication period, n (%) tolcapone first 9 (45%) 3 (33%) 6 (55%) 0.34
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Note: Values are mean (standard deviation). p-Values are unadjusted for multiple comparison; ppm = parts per million.

3.2. Treatment effects on smoking rate and subjective measures

There were no treatment effects on smoking rate during the run-up period or on craving, withdrawal, mood, or side effects (see Table 2). Common side effects on tolcapone were urine discoloration (n = 21), irritability (n = 9) and excessive dreaming (n = 9); all were rated as mild or moderate. There were no significant interactions with genotype.

Table 2.

Smoking rate, subjective measures, and behavioral performance and BOLD signal change during N-back working memory task by treatment condition for the full sample (n = 20) and by genotype (val/val [n = 9] and val/met [n = 11]).

Measure Full sample
 Val/val
 Val/met
 Interaction p-value
Placebo Tolcapone p-Value Placebo Tolcapone Placebo Tolcapone
CPD during run-up period 14.2 (3.9) 14.3 (4.0) 0.85 14 (4.3) 13.6 (3.8) 14.3 (3.7) 14.9 (4.2) 0.30
Subjective measures
Craving 40.5 (14.7) 42.0 (12.6) 0.40 33.2 (12.8) 35.9 (12.0) 46.4 (13.9) 46.9 (11.1) 0.55
Withdrawal 10.9 (7.9) 10.0 (7.7) 0.43 9.0 (6.8) 8.3 (8.9) 12.4 (8.7) 11.5 (6.6) 0.91
N-back behavioral performancea
Accuracy (# true positives) 49.9 (1.3) 51.8 (0.87) 0.017 48.4 (2.5) 50.1 (1.4) 51.2 (1.1) 53.1 (1.0) 0.9
Median correct RT (ms) 518 (16) 520 (16) 0.88 530 (31) 565 (40) 507 (14) 482 (22) 0.001
N-back BOLD signal changea
MF/CG 0.39 (0.03) 0.37 (0.03) 0.67 0.43 (0.05) 0.29 (0.03) 0.35 (0.04) 0.44 (0.04) 0.001
Right DLPFC 0.27 (0.04) 0.27 (0.03) 0.88 0.37 (0.06) 0.26 (0.05) 0.19 (0.04) 0.28 (0.04) 0.008
Left DLPFC 0.32 (0.03) 0.28 (0.03) 0.18 0.26 (0.05) 0.17 (0.03) 0.37 (0.05) 0.37 (0.05) 0.29
PCC −0.34 (0.05) −0.33 (0.04) 0.98 −0.35 (0.08) −0.31 (0.07) −0.32 (0.05) −0.35 (0.05) 0.10
vmPFC −0.29 (0.06) −0.45 (0.07) 0.002 −0.25 (0.10) −0.61 (0.12) −0.34 (0.07) −0.33 (0.06) 0.002
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Note: Unless otherwise noted, values are mean (standard deviation). CPD, cigarettes per day; MF/CG, dorsal cingulate/medial prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; PCC, posterior cingulate cortex; vmPFC, ventromedial PFC.

a

Values are adjusted mean (standard error) controlling for back level and relevant covariates.

3.3. Treatment effects on working memory and BOLD signal change

There was a small increase in accuracy during tolcapone compared to placebo (p = 0.017; Table 2), but no medication effect on reaction time (p = 0.88). Accuracy decreased and reaction time increased with increasing memory load (ps < 0.0001).

There were no treatment effects on BOLD signal in right or left DLPFC, MF/CG, or PCC. For the vmPFC, there was greater deactivation during tolcapone compared to placebo (p = 0.002; Table 2). BOLD signal was not correlated with accuracy or reaction time during either treatment period (ps > 0.05).

3.4. Exploratory analyses by genotype

A genotype × treatment interaction (p = 0.001) suggested that tolcapone (vs. placebo) improved reaction time in the val/met group (p = 0.009), whereas the val/val group was slower during tolcapone (vs. placebo) (p = 0.04). For BOLD signal, there were genotype × treatment interactions in the MF/CG, right DLPFC, and vmPFC (ps < 0.01), but not PCC or left DLPFC (ps > 0.10; Table 2). For the val/val group, tolcapone (vs. placebo) reduced activation in the task-positive region, MF/CG (p = 0.02) and increased suppression of activation in the task-negative region, vmPFC (ps < 0.001). Conversely, tolcapone (vs. placebo) increased activation in the MF/CG (p = 0.03) and right DLPFC (p = 0.01) for the val/met group.

3.5. Practice effects on working memory and BOLD signal change

There was no change in accuracy or reaction time from the first to the second medication period, irrespective of treatment condition (ps > 0.2).

There was a significant decrease in activation in all five ROIs from the first to the second medication period (ps ≤ 0.001). This effect did not vary by genotype (ps > 0.07). We therefore controlled for session (treatment × treatment order interaction term) in our primary analyses.

4. Discussion

Data from this within-subject proof-of-concept study provide limited evidence for beneficial effects of tolcapone in abstaining smokers. There were no signals for medication effects on smoking rate, subjective craving, withdrawal, or mood, using measures that have been previously validated as sensitive to effects of medications efficacious for smoking cessation (Patterson et al., 2009). There was a very small medication effect on accuracy and no effect on correct reaction time on a working memory task, which previously demonstrated sensitivity to abstinence effects (vs. smoking) and to effects of efficacious medications (Falcone et al., 2013; Loughead et al., 2010). Treatment effects on working memory-related brain activity were observed for only one (vmPFC) out of five ROIs. Although it is possible that a larger sample would have yielded greater power to detect a treatment effect, an a priori power analysis using a moderate effect size observed in our prior study (Loughead et al., 2009), suggested that we had 78% power to detect a main effect of treatment. Therefore, we do not believe that our null findings are the result of low power.

In exploratory analyses, we did observe genotype by treatment interactions for some performance and BOLD signal measures. In the val/val group, tolcapone (vs. placebo) slowed reaction time, increased suppression of activation in vmPFC, and reduced activation in task-positive regions. Among val/met smokers, tolcapone (vs. placebo) reduced reaction time, had no effect on task-negative regions, and increased activation in task-positive regions. Nevertheless, these preliminary data should be interpreted cautiously. First, the genotype groups were small and without independent replication data, it is possible that these findings are spurious. Furthermore, the direction of effects was opposite to what would be predicted based on past evidence. Two studies found that tolcapone improved working memory only among val/val genotypes (Farrell et al., 2012; Giakoumaki et al., 2008). A third study found that tolcapone improved working memory performance and reduced BOLD signal in the DLPFC, but did not interact with COMT genotype for most outcomes (Apud et al., 2007). Importantly, unlike these prior studies, we examined abstaining smokers. Prior work has attributed differences between COMT genotypes to the inverted U-shaped dopamine hypothesis, which suggests that only optimal levels of dopamine improve performance (Arnsten, 2009; Goldman-Rakic et al., 2004). Nicotine withdrawal also alters prefrontal dopamine levels (Grieder et al., 2012; Zhang et al., 2012), which may partially account for inconsistencies between studies.

These discrepancies are not surprising considering failures to replicate other findings with COMT val158met variant. For example, there are reported associations of the COMT val allele with deficits in cognitive performance and task-related brain activity in some studies (Dennis et al., 2010; Egan et al., 2001; Stokes et al., 2011), but not in others (Barnett et al., 2008; Wardle et al., 2013). Similarly, the COMT val allele is associated with smoking in some studies (Munafo et al., 2008), but not others (Mutschler et al., 2013), and the direction of association varies across the positive studies (Colilla et al., 2005; Munafo et al., 2008; Omidvar et al., 2009). Genetic studies, particularly imaging studies, have drawbacks that contribute to these inconsistencies including small samples, focus on single candidate genes, and in some cases, post hoc genotyping.

There are other limitations of this study. We observed significant decreases in BOLD signal from the first to second session in all five ROIs, irrespective of treatment condition. We examined self-report measures that relate to anxiety (PANAS and Withdrawal Scale) by session and no significant difference was found. Practice effects were not observed for performance. Reduced BOLD signal for the second session is consistent with previous reports (Chein and Schneider, 2005; van Raalten et al., 2008) and attributed to task familiarity that may allow reduced the effort to maintain performance during a second exposure to the task. This is an important methodological consideration for within-subject neuroimaging studies of cognitive function. Our study included pre-scan practice to criteria, alternate task form, and randomization of treatment by session to minimize this effect. Without a smoking-as-usual baseline session, any effects may not be specific to treatment effects on abstinence. Also, a longer duration of abstinence may have yielded greater performance deficits or changes in fMRI BOLD signal. Additionally, this study used non-treatment seeking smokers, which may limit the sensitivity for detecting medication effects (Perkins et al., 2008). Nevertheless, the limited effects of tolcapone on craving, withdrawal, and cognitive performance observed in this study do not support further examination of COMT inhibitors as smoking cessation aids.

Acknowledgments

Role of funding source

This research was supported by NIH grant R01 DA26849 from the National Institutes on Drug Abuse. The NIH had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Footnotes

Contributors

Authors CL, JL, and RCG were responsible for the conception and design of the study. Authors PMG, KR, JNV, and RDH managed participant recruitment, data collection, and implemented the protocol. Authors RLA, EPW, KR, JNV, and RDH conducted statistical analyses. Author RLA wrote the initial draft of the manuscript. Authors CL and KR contributed to the revision of subsequent drafts. All authors were involved in writing and revising the manuscript and all have approved the final manuscript.

Conflict of interest

Dr. Lerman has served as a consultant to Pfizer on pharmacogenetic testing for smoking cessation treatment and has received research funding from and consulted for AstraZeneca, Targacept, Pfizer, and GlaxoSmithKline, for work unrelated to this manuscript. No other authors have any potential conflict of interests to declare.

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