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. Author manuscript; available in PMC: 2014 May 20.
Published in final edited form as: Behav Pharmacol. 2013 Sep;24(0):448–458. doi: 10.1097/FBP.0b013e328363d1a4

Rate-Dependent Effects of Monoamine Releasers on Intracranial Self-Stimulation in Rats: Implications for Abuse Liability Assessment

Clayton T Bauer 1, Matthew L Banks 1, Bruce E Blough 1, S Stevens Negus 1
PMCID: PMC4028167  NIHMSID: NIHMS579888  PMID: 23851484

Abstract

“Rate-dependency” in the discipline of behavioral pharmacology describes a phenomenon wherein the effect of a drug on the rate of a behavior varies systematically as a function of the baseline, pre-drug rate of that behavior. Historically, rate-dependency studies have compared drug effects on different baseline rates of behavior maintained either by different schedules of reinforcement or during sequential segments of a fixed-interval schedule. The current experiment generated different baseline rates of behavior by altering frequency of electrical stimulation in an assay of intracranial self-stimulation. Amphetamine and 10 other monoamine releasers were analyzed for their ability to produce rate-dependent effects in this assay. There were three main findings. First, all compounds produced rate-dependent effects at some dose. Second, one parameter of rate-dependency plots (peak Y-intercept of the regression line) correlated with in vitro neurochemical data on selectivity of these compounds to release dopamine versus serotonin (p-value = 0.0223, R2 =0.4997). Lastly, a correlation between peak Y-intercept and breakpoints under a progressive-ratio procedure in nonhuman primates was also significant (p-value = 0.0314, R2 = 0.6374). Overall, these results extended the rate dependent effects of monoamine releasers to behavior maintained under ICSS and suggest that, at least for monoamine releasers, the Y-intercept parameter of rate dependency plots might be a useful metric of drug reward and predictor of drug self-administration metrics of drug reinforcement.

Keywords: Rate-dependency, ICSS, amphetamine, monoamine releasers

INTRODUCTION

“Rate-dependency” in behavioral pharmacology describes a phenomenon in which the effect of a drug on the rate of a behavior varies systematically as a function of the baseline, pre-drug rate of that behavior (Dews, 1958; Sanger and Blackman, 1976; Dews, 1981). Fixed-interval (FI) schedules of reinforcement have played a key role in research on rate-dependent drug effects, because these schedules often engender a wide range of response rates during successive segments of the interval and thereby provide a useful baseline of behavior for assessment of drug effects across baseline rates (Ferster and Skinner, 1957; Sanger and Blackman, 1976). Rate-dependent effects of a given drug dose under FI schedules are typically manifested as some degree of increase in low rates of behavior coupled with smaller increases or with decreases in higher rates of behavior, and this relationship is often displayed as a negatively sloped line on a graph that plots baseline rate on the abscissa (usually expressed “log baseline rate”) as a function of rate after drug administration on the ordinate (usually expressed as “log % baseline rate”) (Dews, 1964; Kelleher and Morse, 1968; McMillan, 1973; Sanger and Blackman, 1976). Linear regression of the resulting plot can then yield parameters such as -slope and Y-intercept, and these parameters can be used for quantitative comparison of rate-dependency across doses of a given drug, across drugs, or across experimental conditions. For example, amphetamine effects on operant responding under FI schedules are often rate-dependent, and the degree of rate-dependency (as indicated by the slope of the rate-dependency regression) typically increases with dose and is more prominent with unpunished than with punished responding (McMillan, 1973; Ksir, 1981). Baseline rate is only one of many variables that may influence drug effects on behavior, and other important variables include the schedule of reinforcement and the maintaining event (Sanger and Blackman, 1976; McKearney, 1981). Nonetheless, consideration of rate-dependence has advanced behavioral pharmacology by promoting analysis of drug effects in terms of quantifiable behavioral variables (Dews, 1981).

Intracranial self-stimulation (ICSS) is the general label for a family of operant conditioning procedures that use electrical brain stimulation as the reinforcing stimulus (Olds and Milner, 1954; Stellar and Stellar, 1985; Shizgal and Murry, 1989; Lewis, 1993). Relative to other commonly used reinforcing stimuli, such as food delivery or drug injections, electrical stimulation is distinguished by the speed of its delivery, the brevity of its effects, and the precise degree of control over its magnitude. For example, stimulus parameters commonly used in ICSS studies employ stimulus delivery within milliseconds of a response, stimulus durations of 0.5 sec or less, and stimulus magnitudes that vary across frequency or amplitude increments as low as 0.05 log units. These characteristics have been exploited to develop ICSS procedures that, like FI schedules with more conventional reinforcers, can engender a wide range of baseline behavioral rates during relatively short (10 min) experimental sessions (Carlezon and Chartoff, 2007; Vlachou and Markou, 2011). However, data from ICSS experiments have generally not been viewed through the lens of rate-dependency analysis. Rather, the most common approaches to data analysis focus on low rates of responding maintained by low stimulus magnitudes to determine “thresholds” of stimulation required to maintain responding (Miliaressis et al., 1986). Drug-induced changes in threshold are often interpreted as evidence of “reward” (if a drug increases low rates of responding and decreases threshold) or “anhedonia” (if a drug decreases low to intermediate rates of responding and increases threshold); moreover, drug-induced changes in low rates of responding are often distinguished from drug effects on high rates of responding, which are often interpreted as evidence of “motoric” or “performance” effects (Stellar and Rice, 1989; Lewis, 1993; Carlezon and Chartoff, 2007; Vlachou and Markou, 2011). However, the contribution of rate-dependency to drug effects in other behavioral procedures suggests that rate-dependency might also contribute to drug effects on ICSS, such that drug-induced changes in low- and high-rates of ICSS might reflect a common rate-dependent process rather than distinct effects on distinct processes such as sensitivity to brain stimulation and motor capacity for performing the response.

One application of ICSS has been to assess rewarding drug effects that may contribute to abuse potential (Kornetsky and Esposito, 1979; Vlachou and Markou, 2011), and we recently suggested that this application of ICSS might benefit from strategies for data analysis that integrate both rate-increasing and rate-decreasing drug effects (Bauer et al., 2013). Rate-dependency provides both a conceptual framework and analytic approach for achieving that integration. Accordingly, the aim of the present report was to apply rate-dependency analysis to a previously published ICSS data set of 11 monoamine releasers (Bauer et al., 2012). The drugs span a >8000-fold range of pharmacological selectivity for releasing dopamine/norepinephrine vs. serotonin, and includes relatively dopamine/norepinephrine-selective releasers (e.g. PAL-353 and amphetamine), the serotonin-selective releaser fenfluramine, and a series of releasers with graded selectivities between these extremes. Rate-dependency plots were generated from mean ICSS data for each dose of each drug as described previously with morphine (Altarifi and Negus, 2011), and these plots were then submitted to regression analysis to compare their -slopes and Y-intercepts. Parameters of rate-dependency plots were correlated with dose within each drug, with pharmacological selectivity across drugs, and with data from nonhuman primate drug self-administration assays of abuse-related effects.

MATERIALS and METHODS

Subjects

Methodological details of this study were reported previously [Bauer, 2012]. Briefly, 38 adult male Sprague Dawley rats (Harlan, Frederick, MD, USA) were equipped with stimulating electrodes that targeted the left medial forebrain bundle at the level of the lateral hypothalamus. All rats had free access to food and water and were housed individually on a 12 hour light-dark cycle (lights on from 6 a.m. – 6 p.m.) in a facility accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care. Animal maintenance accorded with The National Institutes of Health guidelines on care and use of animal subjects in research (National Research Council, 2011). Experimental protocols were approved by the Virginia Commonwealth University Institutional Animal Care and Use Committee.

Apparatus

Operant chambers consisted of sound-attenuating boxes containing modular acrylic and metal test chambers [Bauer, 2012]. Each chamber had a response lever, three stimulus lights (red, yellow, and green) centered above the response lever, a house light, and an ICSS stimulator (Med Associates, St. Albans, VT, USA). Bipolar cables routed through a swivel-commutator connected the stimulator to the electrode (Model SL2C, Plastics One, Roanoke, VA, USA). Med-PC IV computer software controlled all programming parameters and data collection (Med Associates).

Training

Animals were trained to respond on an FR 1 schedule for the delivery of a 0.5-s train of square-wave cathodal pulses (0.1ms/pulse). Under the terminal schedule, daily experimental sessions consisted of multiple 10-min components, and each component consisted of 10 1-min trials. The stimulation frequency was 158Hz (2.2 log Hz) for the first trial and decreased 0.05 log units during each of the subsequent 9 trials to a final frequency of 56 Hz (1.75 log Hz). Stimulation intensity was individually manipulated to identify an intensity that maintained greater than half-maximal responding for 5 of the 10 trials. Each trial began with a 10-s time out period, during which responding had no scheduled consequences, and 5 non-contingent stimulations at the designated frequency were delivered at 1 sec intervals during the last 5 sec of the time out. During the remaining 50 sec of each trial, responding produced both intracranial stimulation at the designated frequency and illumination of the lever lights under the FR 1 schedule as described above. Thus, each component generated 10 different response rates maintained by 10 different brain stimulation frequencies. Training continued until mean frequency-rate curves were not statistically different over three days of training as indicated by lack of a significant effect of “day” in a two-way analysis of variance (ANOVA) with frequency and day as the two variables (see data analysis).

Testing

Test sessions consisted of three consecutive “baseline” components followed first by a 20-min treatment period and then by three consecutive “test” components. A single dose of a test drug or drug mixture was administered at the beginning of the treatment period, immediately after the baseline components and before the test components. All test drugs are listed in Table 2 in the order of their in vitro selectivities for promoting release of dopamine vs. serotonin (for all drugs, selectivities to release dopamine and norepinephrine were similar; see Rothman et al., 2001; Rothman et al., 2002; Rothman et al., 2005; Wee et al., 2005; Wang and Woolverton, 2007). Also tested were mixtures of the dopamine-selective releaser PAL-353 and the serotonin-selective releaser fenfluramine in proportions of 1:1, 1:3 and 1:10 PAL-353:fenfluramine. These proportions were based on the relative potencies of PAL-353 and fenfluramine to alter ICSS and were intended to permit assessment of PAL-353 in combination with relatively low, intermediate and high proportions of fenfluramine. Test sessions were usually conducted on Tuesdays and Fridays, and three-component training sessions were conducted all other weekdays. Each drug or drug mixture was tested in a group of 5–7 rats, such that all subjects in the group received all doses, and the order of drug dose was varied across subjects using a Latin-Square design. Any given rat was used to test two to three drugs. The order of drug testing was irregular across rats, and experiments with any one drug or drug mixture in a given rat were completed before progressing to another. Tests with different drugs within a given rat were separated by at least one week, and during this inter-drug interval, a saline/vehicle test session was conducted to assure that previous treatments did not alter ICSS measures.

Table 2.

Peak slope and Y-intercept parameters (95% confidence limits) from the rate-dependency plots of each drug. Note that peak values for the two rate-dependency plot parameters were sometimes obtained at different drug doses. Pharmacological selectivity of each drug to release dopamine vs. serotonin is also shown. These values were generated from in vitro synaptosome preparations taken from rat caudate nuclei (DA) or whole brain minus caudate and cerebellum (5HT) (references below).

Drug Selectivity Dose Slope Y-intercept
PAL-353 803 1.0 −0.6888 (−0.8901 to −0.4874) 2.715 (2.587 to 2.844)
Amphetamine 711 1.0 −0.9340 (−0.9741 to −0.8939) 2.903 (2.869 to 2.936)
Phenmetrazine 372 3.2 -- 2.723 (2.628 to 2.817)
10 −0.7550 (−0.8566 to −0.6534) --
Methamphetamine 301 1.0 −0.8838 (−0.9326 to −0.8349) 2.849 (2.815 to 2.884)
PAL-314 6.54 3.2 -- 2.750 (2.678 to 2.822)
10 −0.8907 (−0.9525 to −0.8289) --
PAL-313 1.24 1.0 -- 2.377 (2.288 to 2.466)
3.2 −0.5480 (−0.6450 to −0.4509) --
+MDMA 0.523 3.2 −0.7982 (−0.8512 to −0.7452) 2.600 (2.563 to 2.637)
PAL-287 0.273 0.32 -- 2.263 (2.085 to 2.440)
3.2 −0.3850 (−0.5315 to −0.2386) --
±MDMA 0.155 3.2 −0.9092 (−9.765 to −0.8419) 2.739 (2.686 to 2.793)
−MDMA 0.093 10 −0.6109 (−0.7893 to −0.4324) 2.379 (2.256 to 2.502)
1.0 Fenfluramine <0.011 1.0 0.3542 (0.2628 to 0.4456) 1.599* (1.537 to 1.661)
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*

Fenfluramine produced a positive slope. Therefore, “peak” effect is lower than 2.0.

Citations

Data Analysis

The primary dependent measure was the reinforcement rate in stimulations/trial. Raw reinforcement rates were normalized to the maximum control rate (MCR) for each subject on each day, where MCR was defined as the mean of the maximal rates observed during the second and third “baseline” components for that day. Therefore, %MCR was equal to (response rate during a frequency trial) / (maximum control rate) × 100. Mean±SEM MCRs were 58.6±1.3 stimulations per trial. ICSS rates, expressed as %MCR, for each frequency were averaged across (a) the second and third baseline components and (b) across all three test components for each rat and then across rats to yield curves relating brain stimulation frequency to baseline and test ICSS rates for each experimental manipulation. These baseline and test data were then used to generate a rate-dependency plot where the x-axis was log baseline rate and the y-axis was log [(test rate) / (baseline rate) * 100]. Each rate-dependency plot consisted of 10 points for baseline and test rates maintained by each of the 10 different brain stimulation frequencies. Each plot was then submitted to linear regression analysis to determine two parameters: (1) the slope (expressed as -slope so that increasingly steep slopes were expressed as increasingly positive numbers), and (2) Y-intercept (expressed as the intercept at x=1, where the baseline rate equaled 10% MCR, and log baseline rate=1). The effects of a given dose of a given drug were considered to be “rate-dependent” if the 95% confidence limits of the slope did not include “0.”-Slope or Y-intercept parameters between doses of a given drug or between drugs were considered to be significantly different if 95% confidence limits did not overlap.

For each drug, the -slope and Y-intercept parameters were graphed as a function of drug dose (log scale). In addition, rate-dependency across drugs was assessed by correlating peak -slope (for any dose of each drug) or peak Y-intercept (for any dose of each drug) with log in vitro selectivity to release of dopamine vs. serotonin. Data were analyzed by linear regression and a Pearson correlation test. A p-value <0.05 was determined to be significant for both the slope of the regression line and for the Pearson correlation test. All regression analyses were conducted using Prism 5.0c for Mac OS X (GraphPad Software, La Jolla, CA).

Drugs

(+)-Amphetamine sulfate and (+)-methamphetamine HCl were provided by the National Institute on Drug Abuse Drug Supply Program (Bethesda, MD). (±)-Fenfluramine HCl was purchased from Sigma Chemical Co. (St. Louis, MO). All other compounds were synthesized as the fumarate or HCl salts by Dr. Bruce Blough (Research Triangle Park, NC). All compounds were prepared in sterile saline and administered intraperitoneally (I.P.). Doses are expressed in terms of the salt forms above.

RESULTS

Figure 1A shows the effects of a 30-fold range of amphetamine doses on ICSS frequency-rate curves (previously published - see Bauer et al. [2012] for time course, statistics, and other details). Figure 1B shows the same data graphed as rate-dependency plots. Table 1 shows that all 4 doses of amphetamine produced rate-dependent effects (insofar as 95% confidence limits of the rate-dependency slopes did not include “0”), but saline (vehicle) did not. Table 1 also shows that amphetamine produced dose-dependent increases in both –slope and Y-intercept parameters of the rate-dependency plot. The dose-dependency of amphetamine effects on these parameters of the rate-dependency plot is also shown in figure 1C.

Figure 1. Rate-dependent effects of amphetamine on ICSS.

Figure 1

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A. Previously published figure (Bauer, 2012) showing the effects of amphetamine on ICSS frequency-rate curves. Abscissa: Frequency of stimulation expressed at Log Hz. Ordinate: ICSS rate expressed as % Maximum Control Rate (%MCR). B. Transformation of data from Panel A into rate-dependency plots showing linear regression lines drawn through points generated by vehicle and 4 doses of amphetamine. Abscissa: Log (baseline rate). Ordinate: Log (% baseline rate). Line at Y=2.0 indicates no change from baseline rates, while line at X=1.0 indicates the position of the Y-intercept values used in Panel C. C. -slope and Y-intercept values generated by regression lines in Panel B are plotted against dose of amphetamine. Abscissa: Dose of amphetamine in mg/kg (log scale). Left ordinate: -Slope. Right ordinate: Y-intercept.

Table 1.

Slope and Y-intercept values (95% confidence limits) for rate-dependency plots of amphetamine effects on ICSS shown in Figure 1C. Note that slope values in Figure 1C are graphed as “-slope” to yield increasingly positive numbers for increasingly steep slopes.

Treatment Slope Y-intercept
Vehicle 0.0182 (−0.2217 to 0.2581) 1.936 (1.748 to 2.124)
0.032 Amphetamine −0.3357† (−0.5020 to −0.1695) 2.290* (2.170 to 2.411)
0.1 Amphetamine −0.4112† (−0.7089 to −0.1135) 2.365 (2.099 to 2.631)
0.32 Amphetamine −0.7528†* (−1.013 to −0.4927) 2.736* (2.515 to 2.958)
1.0 Amphetamine −0.9340†* (−0.9741 to −0.8939) 2.903* (2.869 to 2.936)
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†

Indicates significant rate-dependence as indicated by slope values that do not include “0” in the 95% confidence limits.

*

Indicates significantly different from vehicle as indicated by non-overlapping confidence intervals.

In Figure 2, panels A–C show effects of representative high doses of the dopamine/norepinephrine-selective releaser PAL-353, the non-selective dopamine/norepinephrine/serotonin releaser PAL-287, and the serotonin-selective releaser fenfluramine on ICSS frequency-rate curves (see Bauer et al. [2012] for other details). Figure 2D shows rate-dependency plots for the effects of these three compounds, and linear regression analyses demonstrated that effects of PAL-353, PAL-287, and fenfluramine on ICSS were rate-dependent (see below and Table 2). Figure 3 shows the -slope and Y-intercept data produced by the rate-dependency plots as a function of dose for all doses of PAL-353, PAL-287, fenfluramine and 6 of the other releasers examined (amphetamine was previously shown in Fig. 1; ±MDMA data (not shown) can be found in Table 2).

Figure 2. Rate-dependent effects of selected doses of PAL-353, PAL-287, and fenfluramine on ICSS.

Figure 2

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A–C. Effects of 1.0 mg/kg PAL-353, 3.2 mg/kg PAL-287 and 3.2 mg/kg fenfluramine on ICSS frequency-rates curve compared to the pre-drug baselines. Abscissae: Brain stimulation frequency expressed as Log Hz. Ordinates: ICSS rate expressed as % Maximum Control Rate (%MCR). D. Transformation of data from Panels A–C into rate-dependency plots showing linear regression lines drawn through points generated by the three drugs. Abscissa: Log (baseline rate). Ordinate: Log (% baseline rate).

Figure 3. Rate-dependency plot parameters as a function of dose for nine of the monoamine releasers tested.

Figure 3

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Abscissae: dose in mg/kg (log scale). Left ordinates: -Slope. Right ordinates: Y-intercept. Amphetamine (shown previously in Fig. 1) and rMDMA are omitted.

To further examine the relationship between rate-dependency and pharmacological selectivity, the peak effects of each drug on -slope and Y-intercept parameters were correlated with in vitro measures of pharmacological selectivity. Table 2 shows the peak effects of each drug on slope and Y-intercept parameters; note that, for some drugs, peak effects on these two parameters were produced by different doses. All drugs produced rate-dependent effects, but the profile of rate-dependency varied across drugs. Specifically, all drugs except fenfluramine produced negatively sloped rate-dependency plots, but the slopes and Y-intercepts varied across drugs. Fenfluramine produced a slope that was significantly different from zero (at a single intermediate dose [1.0 mg/kg] shown in Table 2) thus fulfilling our definition of rate-dependency; however, the slope of this regression line was positive unlike the other drugs tested in this procedure. Figure 4 shows the correlation between these -slope and Y-intercept parameters and pharmacological selectivity (data for fenfluramine were excluded from the correlation because selectivity has not been precisely quantified due to very low potency to release dopamine (Rothman et al., 2001)). The correlation between selectivity and -slope was not statistically significant (Pearson r = 0.4164, R square = 0.1733, p-value = 0.2314), but the correlation between pharmacological selectivity and Y-intercept achieved significance (Pearson r = 0.7069, R square = 0.4997, p-value = 0.0223).

Figure 4. Rate-dependency plot parameters as a function of pharmacological selectivity to release dopamine vs. serotonin.

Figure 4

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Abscissae: Pharmacological selectivity expressed as log (in vitro potency to release serotonin ÷ in vitro potency to release dopamine) as shown in Table 2. Ordinate (panel A): Peak –slope for any dose of each drug as shown in Table 2. Ordinate (panel B): Peak Y-intercept for any dose of each drug as shown in Table 2. *Asterisk indicates that fenfluramine data were not included in the correlation because its in vitro potency to release dopamine has not been precisely quantified.

The relationship between rate-dependency and pharmacological selectivity was also examined with mixtures of the dopamine-selective releaser PAL-353 and the serotonin-selective releaser fenfluramine. Figure 5 panels A–C show effects of representative high doses of 1:1, 1:3 and 1:10 mixtures of PAL-353 and fenfluramine on ICSS frequency-rate curves (previously published - see Bauer et al. [2012] for other details). Figure 5, panel D, shows the rate-dependency plots for these doses of these mixtures. Figure 6 shows the relationship between mixture dose and the rate-dependency plot parameters –slope and Y-intercept for each mixture. Table 3 shows the peak effects on slope and Y-intercept produced by any dose of each mixture. All three mixtures produced rate-dependent effects at some dose insofar as 95% confidence limits of the rate-dependency slopes did not include “0.” Peak slopes of the rate-dependency plots were not different across mixtures; however, the 1:1 mixture produces the greatest increase in Y-intercept values, and mixtures with higher fenfluramine proportions produced progressively smaller increases in Y-intercept.

Figure 5. Rate-dependent effects of selected doses of PAL-353/fenfluramine mixtures on ICSS.

Figure 5

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A–C. Effects of 1:1, 1:3, and 1:10 mixtures of PAL-353/fenfluramine on ICSS frequency-rate curves compared to the pre-drug baselines. Abscissae: Brain stimulation frequency in log Hz. Ordinate: ICSS rate expressed as % Maximum Control Rate (%MCR). D. Transformation of data from Panels A–C into rate-dependency plots showing linear regression lines drawn through points generated by the mixtures. Abscissa: Log (baseline rate). Ordinate: Log (% baseline rate).

Figure 6. Rate-dependency plot parameters as a function of dose for 1:1, 1:3 and 1:10 mixtures of PAL-353 and fenfluramine.

Figure 6

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Abscissae: dose of PAL-353 in the mixture (log scale). The corresponding fenfluramine dose was determined by the mixture ratio. Left ordinates: -Slope. Right ordinates: Y-intercept.

Table 3.

Peak slope and Y-intercept (and 95% confidence limits) for mixtures.

Proportion PAL-353:Fenfluramine Dose of PAL-353 Slope Y-intercept
1:1 Mixture 1.0 −0.9132 (−0.9303 to −0.8962) 2.862 (2.850 to 2.874)
1:3 Mixture 1.0 −0.9075 (−0.9944 to −0.8206) 2.692 (2.629 to 2.755)$
1:10 Mixture 0.1 -- 2.370 (2.268 to 2.471)$
1.0 −0.8040 (−0.9283 to −0.6797) --
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$

Significantly different from 1:1 Mixture.

DISCUSSION

Rate-dependence and ICSS

Previous studies have identified rate-dependent effects of amphetamine or methamphetamine under a wide range of conditions (Barrett, 1974; Dews, 1958; Harris et al., 1978; Kelleher and Morse, 1968; Owen, 1960; Sidman, 1956; Smith, 1964). The current study using an ICSS procedure in rats confirms and extends these earlier findings in three ways. First, amphetamine and methamphetamine predominately increased low ICSS rates maintained by low brain-stimulation frequencies while having little to no effect on high ICSS rates maintained by high brain-stimulation frequencies. Both drugs produced effects on ICSS that met the criterion for rate-dependence (i.e. slope of the rate-dependency plot significantly different from “0”). Second, amphetamine and methamphetamine effects on parameters of the rate-dependency plot were dose-dependent, with increases in dose producing increases in both -slope and Y-intercept values. Amphetamine has previously been shown to produce dose-dependent changes in rate-dependency under other conditions (Harris et al., 1978). Lastly, the current study expanded assessment of rate-dependency from the prototype amphetamines to nine other monoamine releasers that varied across a >8000-fold range in their pharmacological selectivity to release dopamine/norepinephrine vs. serotonin, and to mixtures of dopamine-selective and a serotonin-selective releaser. At least one dose of each monoamine releaser and mixture produced rate-dependent effects, and this permitted correlation between rate-dependency plot parameters (-slope, Y-intercept) and pharmacological selectivity. Peak -slope values did not correlate with either pharmacological selectivity to release dopamine vs. serotonin or with proportion of the dopamine- vs. serotonin-selective releasers in a mixture. By contrast, peak Y-intercept values did correlate significantly with pharmacological selectivity to release dopamine and were significantly higher for PAL-353/fenfluramine mixtures that contained higher vs. lower proportions of PAL-353. These findings suggest that rate-dependent effects on ICSS may be shared across a wide range of monoamine releasers, and the vertical position of the rate-dependence plot (as defined by Y-intercept) may be an attribute of rate-dependence that could be used to distinguish drugs.

Rate-dependency in ICSS as an index of abuse-related effects

Both clinical studies (Brauer et al., 1996) and preclinical drug self-administration studies (Wee et al., 2005; Wang and Woolverton, 2007) suggest that abuse-related effects of monoamine releasers vary as a function of pharmacological selectivity to release dopamine vs. serotonin. In the present study, the peak Y-intercept parameter of rate-dependency plots in ICSS also correlated with pharmacological selectivity to release dopamine vs. serotonin. Taken together, these findings suggest that peak Y-intercept of rate-dependent drug effects on ICSS in rats may serve as a predictor of other preclinical and clinical measures of abuse liability. To assess that possibility, Figure 7 shows the correlation between peak Y-intercept for seven drugs tested in this study and peak breakpoint maintained by the same seven drugs in rhesus monkeys responding under a progressive-ratio schedule (Wee et al., 2005; Wang and Woolverton, 2007). These data produced a statistically significant correlation (Pearson r = 0.7984, R squared = 0.6374, p-value = 0.0314). Data for four other drugs could not be included in the correlation because either (a) they did not reliably maintain self-administration under the progressive-ratio procedure so that a breakpoint could not be determined (-MDMA, fenfluramine), or (b) they have not been tested in monkeys with the progressive-ratio procedure (phenmetrazine, PAL-287). However, phenmetrazine produced a relatively high peak Y-intercept in this study, and it functions as an effective reinforcer in self-administration assays in various species responding under various schedules of reinforcement (Wilson et al., 1971; Corwin et al., 1987; Griffiths et al., 1976) and also has verified abuse liability in humans (Craddock, 1976). Conversely, -MDMA, PAL-287 and fenfluramine produced relatively low peak Y-intercept values, and typically fail to maintain self-administration in animals (Tessel and Woods, 1975; Rothman et al., 2005; Wang and Woolverton, 2007). Thus, the significant correlation presented in Fig. 7, together with the qualitative examples provided above, suggests that peak Y-intercept values from rate-dependency plots may serve as a dependent measure in ICSS that is predictive of reinforcing efficacy in assays of drug self-administration. This possibility is further supported by recent studies with the mu opioid receptor agonist morphine (Altarifi and Negus, 2011). In this study, the same Y-intercept metric of rate dependency also increased as a function of a variable (chronic opioid treatment) that is known to enhance other measures of mu agonist abuse liability (e.g. breakpoints in progressive-ratio drug self-administration procedures in nonhuman primates). Additional work would be needed to assess the predictive validity of ICSS results for drug self-administration results across other classes of drugs or other conditions. Moreover, the present results raise the possibility that procedures other than ICSS (e.g. food-maintained responding under fixed-interval schedules) might offer alternative strategies for measuring rate dependence of drug effects and evaluating the relationship between rate dependence and reinforcing effects in assays of drug self-administration. Nonetheless, these results suggest one approach for using data from ICSS studies to predict outcomes of studies using drug self-administration or other procedures that contribute to abuse liability assessment.

Figure 7. Correlation between breakpoint in monkey self-administration and Y-intercept data generated by rate-dependency analysis of ICSS data from rats.

Figure 7

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Abscissa: Maximum breakpoint maintained by any drug dose under a progressive-ratio schedule of drug self-administration in rhesus monkeys. Ordinate: Peak Y-intercept produced by linear regressions of data on a rate-dependency plot. *-MDMA and fenfluramine did not reliably maintain self-administration in all monkeys under this paradigm. Monkey self-administration data taken from Wee et al., 2005; Wang and Woolverton, 2007; unpublished observations (fenfluramine).

The present results support the proposition that efficacy of drugs to produce abuse-related effects in preclinical assays can be distributed along a continuum, but the relationship of this theoretical continuum to metrics of actual drug abuse remains to be determined. Reinforcing drug effects assessed in drug self-administration procedures are considered sufficiently predictive of abuse liability that they are used to inform regulatory decisions on drug control (Ator and Griffiths, 2003; Carter and Griffiths, 2009; European Medicines Agency, 2006; Food and Drug Administration, 2010), and graded drug effects in ICSS or drug self-administration assays bear at least a superficial correspondence to graded categories of drug scheduling by government entities such as the U.S. Drug Enforcement Agency (e.g. amphetamine and methamphetamine as Schedule II, fenfluramine as Schedule IV [US Department of Justice]). However, regulatory and clinical metrics of abuse do not always correspond with each other or with preclinical metrics. In the class of monoamine releasers considered here, for example, methamphetamine maintained higher ICSS Y-intercepts and higher progressive-ratio breakpoints than (±)-MDMA (present study; Wang and Woolverton, 2007). These data could be interpreted to suggest higher abuse liability for methamphetamine than for MDMA, and such a conclusion would agree with more frequent emergency room mentions and treatment episodes for methamphetamine than for MDMA in the United States (Substance Abuse and Mental Health Services Administration, 2009, 2010). On the other hand, the prevalence of MDMA exceeds that of methamphetamine use among persons aged 12 and older in the United States (Substance Abuse and Mental Health Services Administration, 2010b). Lastly, despite these sources of evidence for differential use and/or abuse of methamphetamine and MDMA, scheduling by the U.S. Drug Enforcement Agency does not distinguish between the abuse liability of these drugs; both are considered to have high abuse liability, and differential placement in Schedule II for methamphetamine and Schedule I for MDMA reflects only the status of methamphetamine as an approved medication for clinical use. Taken together, these findings suggest that future research will be required to clarify the relationship between preclinical evidence for graded efficacies of abuse-related drug effects and different clinical manifestations of actual drug use and abuse.

Implications of rate dependence for mechanisms of drugs effects on ICSS

In assays of ICSS, responding is maintained by delivery of electrical brain stimulation, and effects of monoamine releasers and other drugs on ICSS are often interpreted as reflective of drug-induced changes in sensitivity to the reinforcing stimulus (Esposito et al., 1978; Wise 1980). From this perspective, drug effects on ICSS are considered to be dependent primarily on the magnitude of the reinforcing stimulus and on drug-induced changes in detection of that reinforcing stimulus. However, the observation of rate-dependent drug effects in the present study suggests that monoamine releaser effects on ICSS may be dependent instead on baseline rates of responding, and at least to some degree, independent of reinforcer magnitude. This conclusion resonates with earlier studies finding, for example, that amphetamine produced rate-dependent effects on rates of fixed-interval responding maintained by either food presentation or by stimulus-shock avoidance (e.g. Kelleher and Morse, 1968), suggesting the potential for independence of drug effects from reinforcer type. Moreover, indirect dopamine agonists can increase low rates of responding maintained in drug self-administration assays either by low cocaine doses or by vehicle, suggesting the potential for independence of drug effects from reinforcer magnitude (Barrett et al., 2004; Panlilio et al., 1998). Lastly, recent studies with ICSS have also provided evidence to suggest that stimulant effects reflect processes other than enhanced sensitivity to the electrical stimulus (e.g. decreased sensitivity to the “cost” of responding; Hernandez, 2010). Results of the present study suggest that rate-dependency may provide one source of reinforcer-independent factors that influence drug effects on ICSS.

Acknowledgments

Funding from National Institute of Health grants F30-DA034478 (CTB), R01-DA026946 (SSN), and R01-DA012790 (BEB).

Footnotes

Conflicts of interest: No other conflicts declared.

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