N 168 r 0919

Model 1

Model 1

Model n

Fig. 5. Predictive mathematical models for estimating the elapsed time in hours of last cannabis use based on plasma A9-tetrahydrocannabinol (THC) and 11-nor-9-carboxy-A9-THC (THCCOOH) concentrations by GC/MS. (From ref. 70 with permission.)

THCCOOI-I/THC

Fig. 5. Predictive mathematical models for estimating the elapsed time in hours of last cannabis use based on plasma A9-tetrahydrocannabinol (THC) and 11-nor-9-carboxy-A9-THC (THCCOOH) concentrations by GC/MS. (From ref. 70 with permission.)

crashes that found alcohol to be the only drug with significant adverse effects on driving (31). Moskowitz, reporting during the same time frame, did not specifically address plasma concentrations of THC, but cited many studies that found a relationship between cannabis dose and performance impairment including impaired coordination, tracking, perception, and vigilance in driving simulators and on-the-road tests (63). More recent studies with carefully controlled variables and newer performance measures documented that smoking cannabis at doses of 300 ^g THC/kg, or about 20 mg for the 70-kg man in our example, impaired perceptual motor speed, accuracy, and multitasking, all important requirements for safe driving (64-66). The impairing effects of the 300 ^g/kg dose of THC were similar to those of individuals with blood alcohol concentrations of 0.05 g/dL or greater, the legal driving limit in most European countries. When combined with alcohol, the impairing effects of THC were even greater (66-68). However, most of these studies did not attempt to correlate plasma or blood THC concentrations with observed effects but demonstrated that impairment depended on the time after use, with most subjects showing no impairment 24 hours postdose. Huestis et al. performed controlled administration studies that measured plasma THC concentrations in six individuals who had smoked 15.8- and 33-mg doses of THC in marijuana (69). Concentrations for plasma collected after marijuana smoking were used to construct models for predicting the time of last THC use within 95% confidence intervals (see Fig. 5; refs. 30, 70, and 71). Both Model I, which used plasma THC concentrations, and Model II, which used the ratio of THCCOOH/THC concentrations, were found to predict the time of last use in about 90% of cases from all previously published plasma concentration data, whether analysis was by radioimmuno-

assay (RIA), GC, or GC/MS. These mathematical models were further evaluated in another controlled drug administration study of 38 subjects, each smoking a 2.64% THC cigarette. Of these subjects, 29 smoked a second cigarette 4 hours later (72). Plasma was collected immediately after the first cigarette and up to 6 hours after smoking for analysis of THC and THC-COOH concentrations (N = 717). Accuracy, when applying the combination of Model I and Model Il's 95% confidence intervals, following the first cigarette was 99.5% (413 of 415 specimens had a THC concentration or THC-COOH/ THC ratio that predicted the correct time of use within this interval) with no underesti-mations of time of use and maximum overestimation of 4 minutes. Accuracy when applying the combined models' 95% confidence intervals following the second cigarette was 98.6% (285 of 289 specimens) with no underestimations and the same maximum overestimation. When plasma concentrations of THC were between 0.5 and 2 ng/ mL, Model I alone was 80.5% accurate, and Model II alone was 77.6% accurate. However, Model I had no underestimations, and Model II had time of use for 17 of 76 specimens underestimated with maximum errors up to 1.5 hours, indicating that Model II alone is less reliable when THC concentrations are between 0.5 and 2 ng/mL. If the models were used in combination, predicted times of use were accurate for all cases.

Both models are used frequently in courts of law in many countries to estimate elapsed time since last cannabis use in accident and criminal investigations. They allow decision makers to answer a corollary question: How accurately can you estimate the time of last use of cannabis? Officials can use this information to corroborate or discount the accused person's story. After estimating the time of last use, the time course of performance-impairment data reported in the literature is referenced to support a conclusion of possible impairment or lack of impairment. There are many laboratory, simulator, and on-the-road studies that have shown impairment in tasks required for safe driving when individuals have been under the influence of cannabis (66,68), especially when cannabis is combined with ethanol (73).

The onset of impairing effects of THC lags behind the increase in plasma concentration during absorption; then effects remain relatively constant as the concentration decreases dramatically because of THC distribution and metabolism (1). This concentration-effect relationship, displayed in Fig. 6, is described as a counterclockwise hysteresis. As an example, one can observe two different intensities of effects for tachycardia and the visual analog scale for "feel drug" at 50 ng/mL depending on whether the individual is in the absorption or distribution phase. Plasma THC concentrations appear to be linearly related to the intensity of effects during absorption and elimination, but there is no relationship between concentration and effects during distribution. In the case of drivers, it would be rare for authorities to collect a plasma specimen prior to the initial distribution phase of THC. After smoking cannabis, absorption and distribution are complete in 45-60 minutes. It typically takes longer than this to stop the driver, perform a field sobriety test, and transport the driver to a site for drawing blood. In the scenario we are considering, it would be important to determine the time sequence of events from driving through blood collection to ensure that the driver was in the elimination phase. For instructive purposes, we will consider that the police officer testified that the time of blood collection was more than 1 hour after the driver was stopped and that the driver was under observation during this period, precluding further drug use.

VAS Feel Drag Heart Rate

VAS Feel Drag Heart Rate

0 50 100 150 200 0 50 100 150 200

Fig. 6. Visual analog scale for "How strongly do you feel the drug now?" and heart rate (BPM, beats per minute) measures for a subject after smoking a 3.55% THC cigarette demonstrating a counterclockwise hysteresis for the concentration-effect curves.

0 50 100 150 200 0 50 100 150 200

THC ng/mL

Fig. 6. Visual analog scale for "How strongly do you feel the drug now?" and heart rate (BPM, beats per minute) measures for a subject after smoking a 3.55% THC cigarette demonstrating a counterclockwise hysteresis for the concentration-effect curves.

Early epidemiological approaches relating cannabinoid plasma concentrations to accident risk yielded inconsistent results and were criticized for not including an adequate control group of drivers who were on the same roads at similar times and who did not have driving accidents (1). An improved approach, responsibility analysis, independently assigns culpability for the accident and then statistically compares the odds ratio or risk that an accident could occur for individuals who had cannab-inoids in their system and for those that did not. Culpability analysis proved effective for demonstrating performance impairment with alcohol, but was less successful for cannabinoids for several important reasons. In many cases blood was not drawn for cannabinoid analysis until many hours after an accident or impaired driving incident. During this time the concentration of THC in the plasma decreased rapidly, often falling below the limits of quantification (LOQs) of the methods used for analysis. In many cases, the only analyte identified in plasma was THCCOOH, the inactive metabolite with a much wider window of drug detection than parent THC. Some of the early studies only reported whether cannabinoids were present in blood or urine, not specifying whether measurable THC was found. They used analytical methods with high LOQs, i.e., small windows of detection, and were underpowered to identify increased risk because of insufficient sample size. Drummer et al. successfully employed the empirical approach of culpability analysis and found that the group of drivers who had THC present in blood were three to seven times more likely to be responsible for their accident than drivers whose blood specimens were negative for THC (65,74). Those with THC blood concentrations of 5 ng/mL had the higher probability of causing the accident, with a mean odds ratio of 6.8.

With this body of scientific information, we now can answer the question of whether or not marijuana contributed to the driving impairment of the individual in our example. This individual failed the field sobriety test and had 2 ng/mL of THC in his plasma more than an hour after being stopped by the police. In this case, marijuana most likely contributed to the performance impairment. The issue of whether or not a biological test result alone can be used to document impairment is much more controversial. In many states and countries, per se laws have been established that state that an individual is assumed to be under the influence of cannabis if THC or, in some cases, THCCOOH is found in blood, plasma, or, sometimes, urine. The problem of drugged driving is a serious public health issue requiring additional research to link drug concentrations to ongoing impairment, to determine the best analyte and best biological fluid to monitor, and to decide whether administrative cutoff concentrations are needed.

What if the accused driver claimed that he might have unknowingly ingested food that contained cannabis? If this were true, he might be less culpable and receive less punishment. As mentioned, the ratios of 11-OH-THC to THC concentrations differ following the smoked and oral routes of administration; peak concentrations of 11-OH-THC after smoking are about 10% that of THC and approximately equal after oral administration (1). If 11-OH-THC also was measured in the plasma from the driver in our example and its ratio with THC was approx 1:1, this would provide some evidence to support his story.

If we now change venues from the courtroom to the research center, we can examine how scientists use plasma concentrations to help understand the mechanisms by which cannabinoids affect brain function. Advances in brain imaging using positron emission tomography and magnetic resonance imaging have allowed investigators to observe changes in CBF as a result of THC administration (12,75-77). A question relevant to this area of research might be: How do plasma concentrations of THC following administration of cannabis correlate with changes observed in the brain using imaging techniques? Mathew et al., who studied 47 subjects who received two different intravenous doses of THC or placebo, found that THC had significant effects on global and regional CBF (13). Also, feeling intoxicated accounted for changes in regional CBF better than plasma levels of THC. This finding is not surprising in that the effects on the brain would be expected to have a more contemporaneous relationship with related physiological processes in the brain. However, plasma concentrations provide information about individual differences in processing the same dose of cannabis and offer additional information about the metabolites of THC, such as 11-OH-THC, which is physiologically active. It would also be interesting to examine arterial blood because it has been reported that arterial drug concentrations may be more closely related to brain function than venous concentrations (78). Combining pharmacokinetic measures with brain imaging following controlled administration of cannabis is a new area of research that promises to provide interesting scientific information by examining the process of drug action from ingestion through direct physiological changes in regions of the brain.

A related question may be: What information can plasma THC concentrations give us about receptor function? Recently, cannabinoid receptors, CB1 and CB2, and endogenous cannabinoid neurotransmitters have been characterized, primarily from in vitro and animal studies (79-82). In this line of research, cannabinoids with poten-

tial as pharmacotherapies are often evaluated by first studying their interactions with cannabinoid receptors in animals or in vitro, and then examined in human trials. SR141716 (named rimonabant), the first CB1-selective cannabinoid receptor antagonist, was shown to block many of the effects of THC in animals (83,84). In a controlled clinical study of THC's cardiovascular and subjective effects in humans, Huestis et al. found that a single 90-mg oral dose of rimonabant antagonized increases in heart rate and subjective effects following smoked cannabis (85). It was important to determine whether the observed reductions in effects were a result of a receptor-mediated pharmacodynamic change or simply a pharmacokinetic interaction reducing the available THC. The investigators found that there were no statistically significant differences between peak and area-under-the-curve plasma concentrations of THC in the placebo and active rimonabant groups. Therefore, blockade of tachycardia and subjective effects by rimonabant following smoked marijuana was not a result of an alteration in THC pharmacokinetics. In addition to its role as a pharmacological tool to investigate the endogenous cannabinoid system, the antagonist appears to have potential efficacy in humans for smoking cessation (86) and weight loss (87); phase III trials are ongoing for these medical indications. Other potential therapeutic roles for this antagonist are being actively investigated as well.

Clinical trials are evaluating the efficacy of THC, cannabidiol, and other cannab-inoids in the treatment of nausea after cancer chemotherapy, appetite loss, multiple sclerosis, and neuropathic pain (16). A common clinical question might be: How will monitoring plasma cannabinoid concentrations aid clinical management of these patients? As with any new pharmaceutical preparation, it is necessary to study the drug's pharmacokinetics to more clearly understand required doses, frequency of dosing, contributions of metabolites to effects or toxicity, elimination profiles, and metabolism and excretion in different populations, including newborns, children, ethnic groups, diseased individuals, and the elderly. For example, one must determine the median effective dose, ED50, for these populations to assist clinicians who must prescribe doses that will be efficacious but avoid toxicity.

Another concern of clinicians prescribing medications is abuse liability. It has been shown that the route of administration affects the abuse liability of a drug (88). As discussed above, inhalation of smoked cannabis, which results in rapid increases in THC concentrations, can be an effective way for individuals to titrate their THC dose, but may increase its abuse liability. Most clinical trials are evaluating oral, sublingual, or inhaler formulations to better control dose and reduce toxic side effects from smoking. This is expected to reduce the abuse liability as well. Well-designed clinical trials that include pharmacokinetic analyses in tandem with clinical assessment of patients are needed to establish the efficacy and pharmacokinetics of these new preparations and new delivery routes.

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