Full length articleAbuse liability assessment of an e-cigarette refill liquid using intracranial self-stimulation and self-administration models in rats
Introduction
Electronic cigarettes (ECs) are devices that deliver an inhalable aerosol containing nicotine and other constituents (e.g., propylene glycol, minor alkaloids, flavorants; Brandon et al., 2015, Harrell et al., 2014, Orellana-Barrios et al., 2015, Walton et al., 2015). ECs are being marketed as a safer or less addictive alternative to conventional tobacco cigarettes despite the lack of scientific evidence to support these claims (Brandon et al., 2015, Harrell et al., 2014, Orellana-Barrios et al., 2015, Walton et al., 2015). In fact, there is concern that ECs could increase the health burden of tobacco dependence by undermining prevention or cessation efforts (Brandon et al., 2015, Lauterstein et al., 2014, Orellana-Barrios et al., 2015, Walton et al., 2015). Despite the unknown health consequences of ECs, their use is rapidly increasing, particularly among adolescents and current smokers (Lauterstein et al., 2014, Porter et al., 2015). For example, EC use tripled in high school and middle school students between 2013 and 2014, and ECs are now more popular than tobacco cigarettes in youth (Arrazola et al., 2015). In light of these issues, the FDA Center for Tobacco Products (CTP) has the authority to regulate ECs under the Family Smoking Prevention and Tobacco Control Act (FSPTCA), which also provides the FDA CTP regulatory authority over cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco. Establishing methodology for evaluating the relative abuse liability and adverse effects of ECs is therefore essential for informing potential FDA CTP regulatory policy regarding these products and for anticipating the impact of ECs on public health (Brandon et al., 2015, Breland et al., 2014, Cobb et al., 2015).
Preclinical models are crucial for tobacco product evaluation because they can address issues that cannot be studied experimentally in humans (Donny et al., 2012). Most preclinical models of tobacco addiction involve administration of nicotine and/or other constituents (e.g., minor alkaloids, acetaldehyde) in isolation from the thousands of other chemicals in tobacco. This approach may not be sufficient to evaluate the abuse liability of tobacco products because other compounds may contribute to tobacco abuse, either positively or negatively. Ultimately, it is the collective action of these compounds in tobacco, smoke, or EC aerosol that determines the abuse liability of a product (Brennan et al., 2013a, Brennan et al., 2014, Harris et al., 2012, Harris et al., 2015b).
To address these limitations, our laboratory and others have evaluated the addiction-related effects of extracts that are derived directly from tobacco or tobacco smoke and contain an extensive mixture of tobacco constituents (Ambrose et al., 2007, Brennan et al., 2013a, Brennan et al., 2014, Brennan et al., 2013c, Costello et al., 2014, Harris et al., 2012, Harris et al., 2015b, Touiki et al., 2007). Several of these studies have reported greater abuse liability for extracts compared to nicotine alone (e.g., Brennan et al., 2013a, Brennan et al., 2014, Costello et al., 2014). One interpretation is that certain non-nicotine constituents present in extracts (e.g., minor alkaloids, MAO inhibitors) contribute to the greater abuse liability because they can mimic or enhance nicotine’s addiction-related effects when studied in isolation (Bardo et al., 1999, Belluzzi et al., 2005, Dwoskin et al., 1999, Foddai et al., 2004, Guillem et al., 2005, Villegier et al., 2007). Many EC liquids also contain behaviorally active non-nicotine constituents (Etter et al., 2013, Goniewicz et al., 2014, Kosmider et al., 2014). In addition to the same minor alkaloids present in tobacco smoke, some EC liquids contain acetaldehyde, which is self-administered by rats (Myers et al., 1982, Myers et al., 1984, Takayama and Uyeno, 1985) and can enhance the reinforcing and other behavioral effects of nicotine (Belluzzi et al., 2005, Cao et al., 2007). Also, a common vehicle in EC liquids is propylene glycol, which is self-administered in alcohol-preferring rodents (Hillman and Schneider, 1975) and can also have sedative or anxiolytic effects (Da Silva and Elisabetsky, 2001, Lin et al., 1998, Singh et al., 1982, Zaroslinski et al., 1971). To our knowledge, preclinical studies of the abuse liability of EC liquids have not yet been conducted.
The primary goal of the present study was to compare the effects of nicotine alone and nicotine dose-equivalent concentrations of EC liquid in animal models of tobacco addiction. We used a product (Aroma E-Juice Dark Honey Whole Tobacco Alkaloid (WTA)) that is designed to more closely simulate traditional tobacco cigarettes than typical ECs by including higher levels of minor alkaloids than other ECs (www.aromaejuice.com). As such, we hypothesized that this EC liquid would exhibit greater abuse liability than nicotine alone.
We assessed abuse liability using two common behavioral models. The first involved examining the acute effects of nicotine alone and EC liquid in an intracranial self-stimulation (ICSS) assay. Low to moderate doses of nicotine and other addictive drugs lower the minimal (i.e., threshold) electrical stimulation intensity that supports ICSS (e.g., Harrison et al., 2002, Huston-Lyons and Kornetsky, 1992, Kornetsky et al., 1979, Negus and Miller, 2014, Paterson et al., 2008). This may reflect the ability of drugs to enhance the reinforcing effects of non-drug stimuli (e.g., sensory stimuli, food), a phenomenon that may contribute to addiction (Caggiula et al., 2009, Chaudhri et al., 2006, Wise, 2002). This assay provides excellent predictive validity for identifying whether or not a drug will be abused in humans (nominal scaling of drugs), as well as for identifying the relative degree of abuse potential between drugs (ordinal or ratio scaling of drugs; Kornetsky and Esposito, 1979, Kornetsky et al., 1979, Negus and Miller, 2014). Further supporting the sensitivity of this measure, some addictive drugs that do not produce addiction-related effects in other assays (e.g., hallucinogens) nonetheless reduce ICSS thresholds (Wise, 1996, Wise, 2002, Wise et al., 1992). At high doses, nicotine and other drugs disrupt brain reinforcement systems and elevate ICSS thresholds (Fowler et al., 2011, Kenny et al., 2003, Spiller et al., 2009). This represents a putative measure of a drug’s aversive or anhedonic effects that can limit its intake (Fowler and Kenny, 2012, Fowler and Kenny, 2013, Fowler et al., 2011). The relative abuse liability of nicotine alone and EC liquid was also examined in an i.v. self-administration (SA) assay. Differences in rate of acquisition of SA and resistance of consumption to increases in response requirements (i.e., elasticity of demand) were assessed. Combined, these behavioral models provide convergent evidence for the abuse liability of nicotine (see Fowler et al., 2011) and SA is specifically recommended by the FDA for comparing the relative abuse liability of novel compounds to established drugs (Food and Drug Administration, 2010). We also compared formulations in terms of their binding and activation of nicotinic acetylcholine receptors (nAChRs) and nicotine pharmacokinetics to determine whether these factors might mediate the observed behavioral effects.
Section snippets
Animals
Male adult Holtzman rats (Harlan, Indianapolis, IN) weighing 300-350 g at arrival were used. Upon arrival, all rats were individually housed in a temperature- and humidity controlled colony room with unlimited access to food and water under a reversed 12-h light/dark cycle (lights off at 11:00 h) for one week. Rats were then food restricted to 18 g/day for the remainder of the experiment. Protocols were approved by the Institutional Animal Care and Use Committee of the Minneapolis Medical Research
EC liquid constituent analysis
Levels of minor alkaloids (expressed as % of nicotine) in EC liquid were either lower (nornicotine, anabasine) or within the range (anatabine) of those reported for Kodiak and Camel Snus smokeless tobacco extracts in our previous study (Harris et al., 2015b) (Table 1).
Phase 1: acute dose-response determinations
Baseline ICSS thresholds (78.5 ± 5.8 μA versus 76.4 ± 5.6 μA) and response latencies (2.20 ± 0.06 s versus 2.17 ± 0.08 s) did not differ between the nicotine alone and EC liquid dose-response determinations.
Analysis of ICSS threshold data
Discussion
Given the dramatic rise in EC use among adolescents and current smokers and the FDA CTP’s intention to regulate ECs, the present study begins to address an urgent need for preclinical research on the behavioral pharmacology of EC liquids. The main findings of the present study were that EC liquid administration decreased ICSS thresholds to a similar degree as nicotine alone, but was less potent than nicotine alone at increasing ICSS thresholds at high doses. In contrast, there were no
Role of funding source
Funding for this study was provided by NIH/NCI grant U19-CA157345 (Hatsukami DH and Shields P, MPI; LeSage MG, PL), NIDA training grant T32 DA007097 (Smethells, JR; Molitor T, PI), and a Career Development Award (MGL) and Translational Research Program (ACH) from the Minneapolis Medical Research Foundation. These funding institutions had no role in the study design, data collection, data analysis, interpretation of the data, manuscript preparation, or decisions to submit the manuscript for
Contributors
MGL and ACH designed and supervised conduct of the study. MS, PM, and JRS conducted the behavioral studies. IS conducted the alkaloid analysis. PRP advised the design and data analysis for the pharmacokinetic assessment. RIV conducted the statistical analyses. MGL and ACH wrote drafts of the manuscript. All authors contributed to and approved the final manuscript.
Conflict of interest
The authors have no conflicts to disclose.
Acknowledgements
The authors thank Danielle Burroughs, Laura Tally, Theresa Harmon, Clare Schmidt, Christine Egan, and Andrew Banal for their excellent technical assistance in conducting the experiment. The authors also thank Drs. Steven Hursh and Pete Roma from the Institutes for Behavior Resources (Baltimore, MD) and Johns Hopkins University School of Medicine for providing the software for demand curve analysis and their assistance with conducting the analysis. Ki determinations and agonist/antagonist
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