Jacob Bell
New Member
Estimating the Time of Last Cannabis Use from Plasma 9-Tetrahydrocannabinol and 11-nor-9-Carboxy-9-Tetrahydrocannabinol Concentrations
Marilyn A. Huestis,1* Allan Barnes,1 and Michael L. Smith2
Background: Knowing the time cannabis was last used
is important for determining impairment in accident
investigations and clinical evaluations. Two models for
predicting time of last cannabis use from single plasma
cannabinoid concentrations–model I, using 9-tetrahydrocannabinol
(THC), and model II, using the concentration
ratio of 11-nor-9-carboxy-THC (THCCOOH) to
THC–were developed and validated from controlled
drug administration studies. Objectives of the current
study were to extend the validation by use of a large
number of plasma samples collected after administration
of single and multiple doses of THC and to examine
the effectiveness of the models at low plasma cannabinoid
concentrations.
Methods: Thirty-eight cannabis users each smoked a
2.64% THC cigarette in the morning, and 30 also smoked
a second cigarette in the afternoon. Blood samples (n
717) were collected at intervals after smoking, and
plasma THC and THCCOOH concentrations measured
by gas chromatography—mass spectrometry. Predicted
times of cannabis smoking, based on each model, were
compared with actual smoking times.
Results: The most accurate approach applied a combination
of models I and II. For all 717 plasma samples,
99% of predicted times of last use were within the 95%
confidence interval, 0.9% were overestimated, and none
were underestimated. For 289 plasma samples collected
after multiple doses, 97% were correct with no underestimates.
All time estimates were correct for 76 plasma
samples with THC concentrations between 0.5 and 2
g/L, a concentration range not previously examined.
Conclusions: This study extends the validation of the
predictive models of time of last cannabis use to include
multiple exposures and low THC concentrations. The
models provide an objective and validated method for
assessing the contribution of cannabis to accidents or
clinical symptoms.
© 2005 American Association for Clinical Chemistry
Cannabis is one of the most widely abused drugs in the
world, second only to alcohol (1 ). As one might expect
with a high prevalence of use in the general population,
cannabis is often involved in accidents and other mishaps
of operations that require skill. For example, Soderstrom
et al. (2 ) reported in 1988 that 34.7% of 1023 patients
admitted to a large city trauma center as a result of
vehicular and nonvehicular accidents had cannabinoids
present in their blood. In a 1992 national safety study,
Crouch et al. (3 ) found that 13% of truck drivers killed in
traffic accidents in the United States had 9-tetrahydrocannabinol
(THC),3 the primary active component in
cannabis, or its metabolite 11-nor-9-carboxy-THC (THCCOOH)
in their blood or urine at autopsy. These and
similar studies have demonstrated a connection between
accidents and cannabis use, but have not established
causation by cannabis, despite the knowledge that individuals
who use cannabis have impaired performance in
driving simulator and on-the-road tests (4—8). In the
driving studies, the strongest decrements were in the
drivers' abilities to concentrate and maintain attention,
estimate time and distance, and demonstrate coordination
on divided attention tasks, all important requirements for
driving (9, 10). Drummer et al. (11 ) did determine causality
in a study of 3398 fatally injured drivers in Australia,
using culpability analysis, and reported that drivers with
blood THC concentrations of 5 g/L or greater were 6.6
times more likely than drug-free drivers to have a fatal
accident. Medical and forensic investigators tasked with
establishing causality and impairment use information
from these and similar studies. Knowing the elapsed time
from last drug use is necessary for applying these data to
an individual case.
Scientists currently evaluate impairment in individual
cases by estimating the time of drug use and relating these
parameters to those found in pharmacodynamic studies
(12 ). Unlike cases involving use of alcohol, impairment of
performance from cannabis use is not easily correlated to
blood or plasma concentrations of THC or its metabolites.
Maximum drug effects may occur later than peak blood
concentrations of THC or its active metabolites. Effects on
the brain continue as blood concentrations of active drug
decrease, a process termed hysteresis (13 ).
Investigations dating back to those by Lemberger (14 )
have attempted to relate blood or plasma cannabinoid
concentrations with the time after smoking or oral ingestion
of cannabis (14—19). Using different methods of
analysis, scientists produced expected time-vs-plasma
concentration profiles but described large inter- and intraindividual
variations in peak values, times to reach
maximum concentrations, and areas under the curve.
Most persons who were infrequent cannabis users had
plasma THC concentrations 1 g/L after 4 h (20 ). Peat
(21 ) introduced results for frequent cannabis users showing
mean (SE) plasma THC and THCCOOH concentrations
of 0.86 (0.22) and 45.8 (13.1) g/L, respectively, for
persons who reported their last cannabis use more than
12 h before blood collection. In a well-controlled study of
6 cannabis users, Huestis et al. (19 ) collected blood
samples from volunteers after they had smoked single
cannabis cigarettes containing 0%, 1.75%, and 3.55% THC.
Participants were housed in a secure medical unit with
dosing delayed until urine concentrations of cannabinoids
were 20 g/L. Conditions that contribute to interindividual
variation in plasma concentrations of THC and its
metabolites were carefully controlled, e.g., cigarette potency,
number of puffs, time between puffs, length of
inhalation, and length of time that smoke was held in the
lungs were standardized, but for safety reasons other
variables, such as depth of inhalation, were not. First
samples were collected at 1 min after smoking and at
frequent intervals up to 168 h. From these data, the
authors developed 2 models to predict time of last cannabis
use within 95% confidence intervals (CIs). The first
model computed the elapsed time between smoking cannabis
and blood collection based on plasma THC concentration
alone, whereas the second model used the plasma
THCCOOH/THC concentration ratio. They applied the
models to results from all published studies at the time
that reported THC and/or THCCOOH concentrations
measured by either RIA or gas chromatography—mass
spectrometry with either internal or external standardization.
They found that the models correctly predicted time
of use within the CI for 90% of samples. The models
appeared to slightly overestimate time immediately after
smoking and underestimate later times. The models were
not challenged with samples having plasma THC concentrations
2 g/L because of analytical limitations at the
time. When applying the models to whole blood concentrations,
it is important to remember that blood THC and
THCCOOH concentrations are lower than in plasma
because of restricted distribution of these analytes into
erythrocytes. The blood-to-plasma ratio is estimated to be
0.5 for living humans, although supporting data are
limited (22, 23).
Scientists in North America, Europe, Australia, and
Canada have applied these predictive models in forensic
and medical situations in which it was important to
estimate the time of last cannabis use. Challenging the
models with samples containing lower THC concentrations
and with other samples collected after multiple
cannabis doses extends the method validation and increases
the usefulness of the models. The current study
examines plasma THC and THCCOOH concentrations in
717 plasma samples collected from 38 males after each
smoked a cannabis cigarette in the morning and from 30
of these same individuals after they had smoked a second
cigarette 4 h later. The original models were evaluated for
accuracy in predicting the time of last cannabis use, the
magnitude of error of estimations, and instances when
actual times were longer or shorter than predicted time
intervals.
Materials and Methods
participants and study design
Descriptions of the participants and study design were
reported previously (24 ). Thirty-eight males with a history
of cannabis use provided informed consent to this
National Institute on Drug Abuse Intramural Research
Program Institutional Review Board—approved protocol.
The study participants were compensated for their time
and effort. All resided on the secure clinical research unit
for at least 1 day before dosing and 2 days afterward. No
drugs were administered until the urine cannabinoid
concentrations were 20 g/L. For safety, participants
were medically evaluated during and for 1 week after
drug administration.
At 0900 on the day of testing, participants received a
single oral dose of placebo (n 10) or up to 90 mg of
rimonabant. Two hours later, they smoked a cannabis
cigarette containing 2.64% THC by weight, estimated to
contain 20 mg of THC. The number of puffs and time
between puffs were standardized. Blood for THC and
THCCOOH assays was drawn from an indwelling venous
catheter in the arm 120 and 5 min before cannabis
smoking and at 2, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110, and
235 min after the start of smoking. Heparinized plasma
was stored at 20 °C until analysis. Four hours after
smoking the first cigarette, 30 of the men smoked a second
cigarette containing 2.64% THC. Blood samples were
collected at the same time intervals as described for the
first dose, except that the 235-min collection was omitted.
The previously published study designed to investigate
the blockade of cannabis effects demonstrated that
rimonabant had no effect on THC and THCCOOH pharmacokinetics
(24 ). The participants receiving rimonabant
and placebo could therefore be grouped together for the
current study and the results generalized to cannabis
users.
Plasma THC and THCCOOH concentrations were determined
in plasma from blood collected from 38 participants
in the morning smoking session and 30 who also
smoked in the afternoon. Occasional samples had THCCOOH
2.5 g/L, the limit of quantification (LOQ) for
the method. In all, 717 plasma THC and 704 plasma
THCCOOH concentrations were evaluated with the two
predictive models.
analytical method
Plasma samples were analyzed for THC and THCCOOH
by a previously published method (25 ). Briefly, trideuterated
THC and THCCOOH internal standards were added
to plasma to improve identification and quantification.
Proteins were precipitated with acetonitrile. The extraction
method used CleanScreen solid-phase extraction columns
(United Chemical Technologies), with THC eluted
with hexane—ethyl acetate—ammonia hydroxide (93
2 by
volume) and THCCOOH eluted with hexane—ethyl acetate
(70:30 by volume). Trifluoroacetyl-THC and trifluoroacetyl-
hexafluoroisopropyl-THCCOOH derivatives in the
same reconstituted extract were injected into a Finnegan/
MAT Model 4023 gas chromatograph—mass spectrometer
operated in the negative-ion chemical ionization mode
with methane as reagent gas, helium as carrier gas, and a
DB 1 capillary column (J & W Scientific). Cannabinoid
concentrations were determined by use of ion m/z 410 for
THC and m/z 422 for THCCOOH. The LOQs were 0.5
g/L for THC and 2.5 g/L for THCCOOH, with CVs
across the analytical range of 4.1%—11% and 4.9%—12%,
respectively.
predictive models
Huestis et al. (19 ) previously published models for estimating
time of last cannabis use. Model I determined time
estimates from plasma THC concentrations and model II
from the plasma THCCOOH/THC concentration ratios.
The formulas are reproduced below, with t representing
the elapsed time in hours between the beginning of
cannabis smoking and blood collection, and CI representing
the 95% confidence interval for the estimate of t. The
subscripts 1 and 2 refer to models I and II, respectively,
and brackets indicate the concentrations of THC or THCCOOH
in g/L:Model I: log t 0.698 log [THC] 0.687
log CI1 log t 1.975
0.0301.006
(log[THC] 0.996)2
89.937
Model II: log t (0.576 log [THCCOOH]/[THC]) 0.176
log CI2 log t 1.975
0.045 1.006
(log[THCCOOH]/[THC] 0.283)2
123.420
These equations were developed from cannabinoid
concentrations in plasma samples collected for up to 168 h
after controlled smoking of a 1.75% and a 3.55% THC
cigarette by each of 6 cannabis users residing continuously
in a secure clinical research unit (19 ). Drug administration
was not initiated until their urine cannabinoid
concentrations were 20 g/L. Table 1 is reproduced
from that report (19 ) and displays the 95% CIs, in hours,
for selected predicted times after smoking using models I
and II. These results are presented to show that the
models predict last use within an interval of time. The
magnitude of the interval is smaller when the elapsed
time between cannabis use and blood collection is shorter.
In the current study, model I was applied to each
plasma sample with a valid THC measurement and
model II to those with valid THC and THCCOOH concentrations.
All analyte results at or above the LOQs of the
method were selected. For each model, the CI for time of
last use was determined. The actual times of smoking
were examined to determine whether they were within
(i.e., correct) or outside (i.e., incorrect) the CI. The following
parameters were calculated for each model: (a) accuracy
[(number of correct time estimates/total number of
estimates) 100%]; (b) the mean magnitude of error
(mean of absolute values of tactual tcalculated for all
incorrect estimates); and (c) the number of samples with
actual times greater than the upper limit of the CI (i.e., the
model estimated a shorter t than actual, termed an underestimate)
or below the lower limit (i.e., the model estimated
a longer t than actual, termed an overestimate) for
each situation examined.
We applied the same evaluations to each sample, using
the models in combination. With this application, estimates
were considered correct if the true value of t fell
within a range defined by the lower limit of the CI of
either model and the upper limits of the CIs, whichever
was highest.
Results
The results for predicted elapsed times between the
beginning of cannabis smoking and blood collection obtained
with model I, model II, and a combination of the
models are summarized in Table 2. The conditions for the
single cigarette study were similar to those in the study
used to produce the models, except that cigarettes contained
2.64% THC instead of either 1.75% or 3.55% THC,
and length of inhalation and the time smoke was held in
the lungs were not controlled. For model I, Table 2 reflects
that, for 392 of 427 samples collected from 38 individuals
after they began smoking the first cigarette, the observed
time fell within the predicted range of elapsed time.
Another way to view this result is that the model was
correct for 91.8% of cases. For those cases that were
overestimated, the average error of actual elapsed times
was 14 min lower than the lowest value in the CI. There
were no underestimates when model I was applied to
data obtained after the first cigarette.
As can also be seen in Table 2, the models provided
similar results after volunteers (n 30) smoked a second
cannabis cigarette in the afternoon. A difference noted
after multiple cigarettes was that 3 of 290 samples had
actual elapsed times longer than predicted by model I, i.e.,
for these 3 samples the time of last use was underestimated.
Model II correctly estimated the time of last smoked
dose for 94.0% of samples after a single cigarette. Unlike
model I, most of the errors (21 of 415 samples) were
underestimates. Estimates were more accurate after multiple
doses.
We evaluated whether the CIs for models I and II could
be combined to produce superior results. Shown in the
lower portion of Table 2 are the accuracy, overestimates,
and underestimates in predicted elapsed times when we
used models I and II in combination. If the actual elapsed
time after the beginning of smoking fell within a range
with a lower limit equal to the lowest predicted confidence
limit for either model and an upper limit equal to
the highest predicted confidence limit for either model,
then results were considered correct. Accuracies were
near 99%, and there were no instances of underestimated
predictions.
One of the limitations mentioned in the introduction
was that the models had not been challenged for plasma
THC concentrations 2 g/L. This is an important limitation
because concentrations in this range are expected in
the first 4 h after cannabis smoking, the timeframe in
which many individuals are impaired and attempt to
perform skilled operations (20 ). In this study, 77 plasma
samples from 26 individuals had THC concentrations in
the range of 0.5—2 g/L. The results of application of the
models to these samples are shown in Table 3. All valid
predictions were accurate with the combination of models
I and II. Independently, the models were not as accurate
for these concentrations as they were for samples with
concentrations 2 g/L: 80.5% compared with 91.2% for
model I, and 77.6% compared with 95.5% for model II.
Discussion
For most forensic and medical applications, the ultimate
goal of determining the time of last cannabis use is to
relate this time to effects of the drug on the body. Effects
vary among individuals, but most people experience the
physiologic effects of increased heart rate and conjuncti-val injection, or bloodshot eyes (26, 27). These effects
begin during smoking and may last up to several hours.
Effects on the brain include euphoria and decrements in
memory, the ability to maintain attention, estimating
time, and coordination on divided attention tasks (23 ).
The length of time that acute effects on the brain continue
has been debated among scientists. Some investigators
have reported subjective and impaired behavioral effects
for up to 3 h after the end of smoking but could not find
significant effects the following day (7, 28—30). Others
have reported decrements in cognitive function and complex
performance tasks for more than 24 h (31—33 ). A
consensus panel of experts that met to determine the
current state of knowledge for effects of cannabis on
driving performance reported that "most behavioral and
physiological effects return to baseline levels within 3—5 h
after drug use, although some investigators have demonstrated
residual effects in specific behaviors such as complex
divided attention tasks for up to 24 h" (23 ). Using the
models and an individual's plasma THC and THCCOOH
concentrations, one may estimate the time the individual
last used cannabis and from this time predict cognitive or
performance decrements.
The results of this study demonstrate that model I
accurately predicts the time of last cannabis use for 90%
of cases and that incorrect predictions are overestimates
after single doses. After multiple doses of cannabis, accuracy
remains high, but 3 of 290 results were underestimated.
Although that number is small, we pay special
attention to these underestimates because in general they
have a greater impact on actual cases than do overestimates.
For example, if a judicial investigator or physician
wants to know whether a person with THC in his or her
blood was impaired at the time of an accident, it is
important to know how long before the accident that
person smoked cannabis. Underestimating this time increases
the potential for falsely accusing an individual of
being impaired at the time of the accident. For our 3 cases,
the underestimates were not serious problems, however,
because the magnitudes were small, ranging from 2 to 11
min. If we used the estimated times in these cases to
predict the effects of cannabis on the individuals, the
errors in time are small compared with the variability in
specific effects and will not impact our decision.
Determining accuracy after a second cigarette is an
important finding because previous studies did not address
applying the models to multiple THC administrations.
Bogusz, in a letter to the editor in the Journal of
Analytical Toxicology (34 ), questioned the applicability of
the predictive models when individuals ingested multiple
doses of cannabis. Huestis and Cone addressed many, but
not all, of his concerns in a response and suggested that
the models be examined in a multiple-dose study (34 ). We
now demonstrate that model I gives accurate predictions
for 90% of plasma samples collected after multiple
cannabis doses.
After single doses, model II was 94.0% accurate, but
most errors (21 of 415 samples) were underestimates.
However, all 21 were from samples collected 3 h after
smoking. Huestis et al. (19 ) noted in the publication of the
models that predictions at longer elapsed times were
occasionally underestimated. For those times that were
underestimated, the magnitude ranged from 2 to 94 min
with a mean of 38 min.
The combined models gave time estimates that were
99.1% accurate with no underestimates. In addition, when
overestimates occurred, they were less than 4 min. For the
subset of samples with THC concentrations between 0.5
and 2.0 g/L and THCCOOH concentrations 2.5 g/L,
the combined-model approach was correct in all instances.
This method of analysis yields a longer estimated
time interval, but greater certainty in prediction. One
important outcome of greater certainty is that underestimates
of time of use were eliminated. For forensic cases in
which finders of fact wish to avoid underestimating time
of use, the combined CI model offers greater accuracy and
is the method of choice.
The cannabis cigarettes used in our study contained
2.64% THC. Individuals smoked 1 puff/min but chose
their own depth of inhalation. This rate is greater than
self-paced smoking. All had increases in heart rate and
subjective drug effects. According to ElSohly et al. (35 ), in
1997 the mean THC concentration in seized cannabis was
4.2%. Of course, cannabis can have a higher THC content.
The models were developed from plasma data collected
after persons smoked a 1.75% and a 3.55% cigarette, the
latter having more THC than used in the current study
(19 ). Although peak concentrations were higher for the
3.55% cigarettes, the times of use fell within a range
similar to that for the lower-dose cigarette. Other studies
also have shown that cannabis users tend to titrate their
dose of drug to maintain the level of intoxication they
prefer (20 ). One might expect users who are free to choose
their own smoking pattern to keep the dose of THC
within a range that produces this effect even if the
cigarette THC content is high. Their plasma THC and
THCCOOH concentration profiles would be similar to
those after smoking of a less potent cigarette. In addition,
the models have been shown to be accurate in many legal
proceedings in multiple countries over the last 13 years
when the times of cannabis use were known.
A common problem in applying the models to actual
situations is that many accident victims die and THC and
THCCOOH concentrations are available only for postmortem
blood. As mentioned in the introduction, the
average antemortem blood-to-plasma ratio is 0.5. One
can estimate a plasma concentration from a blood concentration
by dividing by 0.5 and inserting the plasma
concentration into the formulas to estimate time of last
cannabis use. However, in fatal accident cases, the important
variables are the concentrations of drugs and metabolites
in antemortem plasma; estimating these from postmortem
blood has not been well documented. Giroud et
al. (36 ) studied the concentrations of THC and THCCOOH
in blood and plasma from live patients and found
mean ratios similar to that reported above, specifically
0.67 and 0.59, respectively. These authors also measured
postmortem blood and postmortem "serum" (obtained
from centrifuging postmortem whole blood) cannabinoid
concentrations and found variable ratios with mean
blood-to-serum ratios of 0.45 for THC and 0.37 for THCCOOH.
The causes of this variability can be many, including
changes in serum after death and postmortem redistribution.
The study did have an unavoidable limitation of
being unable to relate postmortem blood and antemortem
plasma analyte concentrations. Errors in converting postmortem
blood to antemortem plasma concentrations may
have an impact on predictions using model I but would
have less effect on predictions using model II because
model II estimates are based on the ratio of THCCOOH to
THC. These 2 cannabinoid molecules are similarly partitioned
between plasma and blood cells, making the ratio
less sensitive to partition variability.
In conclusion, in this study we extended the validation of
2 previously described models for predicting the time of
last cannabis use from plasma THC and THCCOOH
concentrations to samples collected after multiple cannabis
exposures and also those with low THC concentrations.
The predictive models provide an objective and
validated method for assessing the contribution of cannabis
to accidents or clinical symptoms based on cannabinoid
concentrations in a single plasma sample.
This research was supported in part by the Intramural
Research Program of the NIH, NIDA, and by Sanofi-
Aventis. We thank D.A. Gorelick, S.J. Heishman, K.L.
Preston, R.A. Nelson, and E.T. Moolchan, coinvestigators
on the study in which plasma cannabinoid data were
collected.
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physiological, subjective, and performance measures. Pharmacol
Biochem Behav 1990;37:561—5.
32. Leirer VO, Yesavage JA, Morrow DG. Marijuana carryover effects
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36. Giroud C, Menetrey A, Augsburger M, Buclin T, Sanchez-Mazas P,
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Source: Estimating the Time of Last Cannabis Use from Plasma {Delta}9-Tetrahydrocannabinol and 11-nor-9-Carboxy-{Delta}9-Tetrahydrocannabinol Concentrations
Marilyn A. Huestis,1* Allan Barnes,1 and Michael L. Smith2
Background: Knowing the time cannabis was last used
is important for determining impairment in accident
investigations and clinical evaluations. Two models for
predicting time of last cannabis use from single plasma
cannabinoid concentrations–model I, using 9-tetrahydrocannabinol
(THC), and model II, using the concentration
ratio of 11-nor-9-carboxy-THC (THCCOOH) to
THC–were developed and validated from controlled
drug administration studies. Objectives of the current
study were to extend the validation by use of a large
number of plasma samples collected after administration
of single and multiple doses of THC and to examine
the effectiveness of the models at low plasma cannabinoid
concentrations.
Methods: Thirty-eight cannabis users each smoked a
2.64% THC cigarette in the morning, and 30 also smoked
a second cigarette in the afternoon. Blood samples (n
717) were collected at intervals after smoking, and
plasma THC and THCCOOH concentrations measured
by gas chromatography—mass spectrometry. Predicted
times of cannabis smoking, based on each model, were
compared with actual smoking times.
Results: The most accurate approach applied a combination
of models I and II. For all 717 plasma samples,
99% of predicted times of last use were within the 95%
confidence interval, 0.9% were overestimated, and none
were underestimated. For 289 plasma samples collected
after multiple doses, 97% were correct with no underestimates.
All time estimates were correct for 76 plasma
samples with THC concentrations between 0.5 and 2
g/L, a concentration range not previously examined.
Conclusions: This study extends the validation of the
predictive models of time of last cannabis use to include
multiple exposures and low THC concentrations. The
models provide an objective and validated method for
assessing the contribution of cannabis to accidents or
clinical symptoms.
© 2005 American Association for Clinical Chemistry
Cannabis is one of the most widely abused drugs in the
world, second only to alcohol (1 ). As one might expect
with a high prevalence of use in the general population,
cannabis is often involved in accidents and other mishaps
of operations that require skill. For example, Soderstrom
et al. (2 ) reported in 1988 that 34.7% of 1023 patients
admitted to a large city trauma center as a result of
vehicular and nonvehicular accidents had cannabinoids
present in their blood. In a 1992 national safety study,
Crouch et al. (3 ) found that 13% of truck drivers killed in
traffic accidents in the United States had 9-tetrahydrocannabinol
(THC),3 the primary active component in
cannabis, or its metabolite 11-nor-9-carboxy-THC (THCCOOH)
in their blood or urine at autopsy. These and
similar studies have demonstrated a connection between
accidents and cannabis use, but have not established
causation by cannabis, despite the knowledge that individuals
who use cannabis have impaired performance in
driving simulator and on-the-road tests (4—8). In the
driving studies, the strongest decrements were in the
drivers' abilities to concentrate and maintain attention,
estimate time and distance, and demonstrate coordination
on divided attention tasks, all important requirements for
driving (9, 10). Drummer et al. (11 ) did determine causality
in a study of 3398 fatally injured drivers in Australia,
using culpability analysis, and reported that drivers with
blood THC concentrations of 5 g/L or greater were 6.6
times more likely than drug-free drivers to have a fatal
accident. Medical and forensic investigators tasked with
establishing causality and impairment use information
from these and similar studies. Knowing the elapsed time
from last drug use is necessary for applying these data to
an individual case.
Scientists currently evaluate impairment in individual
cases by estimating the time of drug use and relating these
parameters to those found in pharmacodynamic studies
(12 ). Unlike cases involving use of alcohol, impairment of
performance from cannabis use is not easily correlated to
blood or plasma concentrations of THC or its metabolites.
Maximum drug effects may occur later than peak blood
concentrations of THC or its active metabolites. Effects on
the brain continue as blood concentrations of active drug
decrease, a process termed hysteresis (13 ).
Investigations dating back to those by Lemberger (14 )
have attempted to relate blood or plasma cannabinoid
concentrations with the time after smoking or oral ingestion
of cannabis (14—19). Using different methods of
analysis, scientists produced expected time-vs-plasma
concentration profiles but described large inter- and intraindividual
variations in peak values, times to reach
maximum concentrations, and areas under the curve.
Most persons who were infrequent cannabis users had
plasma THC concentrations 1 g/L after 4 h (20 ). Peat
(21 ) introduced results for frequent cannabis users showing
mean (SE) plasma THC and THCCOOH concentrations
of 0.86 (0.22) and 45.8 (13.1) g/L, respectively, for
persons who reported their last cannabis use more than
12 h before blood collection. In a well-controlled study of
6 cannabis users, Huestis et al. (19 ) collected blood
samples from volunteers after they had smoked single
cannabis cigarettes containing 0%, 1.75%, and 3.55% THC.
Participants were housed in a secure medical unit with
dosing delayed until urine concentrations of cannabinoids
were 20 g/L. Conditions that contribute to interindividual
variation in plasma concentrations of THC and its
metabolites were carefully controlled, e.g., cigarette potency,
number of puffs, time between puffs, length of
inhalation, and length of time that smoke was held in the
lungs were standardized, but for safety reasons other
variables, such as depth of inhalation, were not. First
samples were collected at 1 min after smoking and at
frequent intervals up to 168 h. From these data, the
authors developed 2 models to predict time of last cannabis
use within 95% confidence intervals (CIs). The first
model computed the elapsed time between smoking cannabis
and blood collection based on plasma THC concentration
alone, whereas the second model used the plasma
THCCOOH/THC concentration ratio. They applied the
models to results from all published studies at the time
that reported THC and/or THCCOOH concentrations
measured by either RIA or gas chromatography—mass
spectrometry with either internal or external standardization.
They found that the models correctly predicted time
of use within the CI for 90% of samples. The models
appeared to slightly overestimate time immediately after
smoking and underestimate later times. The models were
not challenged with samples having plasma THC concentrations
2 g/L because of analytical limitations at the
time. When applying the models to whole blood concentrations,
it is important to remember that blood THC and
THCCOOH concentrations are lower than in plasma
because of restricted distribution of these analytes into
erythrocytes. The blood-to-plasma ratio is estimated to be
0.5 for living humans, although supporting data are
limited (22, 23).
Scientists in North America, Europe, Australia, and
Canada have applied these predictive models in forensic
and medical situations in which it was important to
estimate the time of last cannabis use. Challenging the
models with samples containing lower THC concentrations
and with other samples collected after multiple
cannabis doses extends the method validation and increases
the usefulness of the models. The current study
examines plasma THC and THCCOOH concentrations in
717 plasma samples collected from 38 males after each
smoked a cannabis cigarette in the morning and from 30
of these same individuals after they had smoked a second
cigarette 4 h later. The original models were evaluated for
accuracy in predicting the time of last cannabis use, the
magnitude of error of estimations, and instances when
actual times were longer or shorter than predicted time
intervals.
Materials and Methods
participants and study design
Descriptions of the participants and study design were
reported previously (24 ). Thirty-eight males with a history
of cannabis use provided informed consent to this
National Institute on Drug Abuse Intramural Research
Program Institutional Review Board—approved protocol.
The study participants were compensated for their time
and effort. All resided on the secure clinical research unit
for at least 1 day before dosing and 2 days afterward. No
drugs were administered until the urine cannabinoid
concentrations were 20 g/L. For safety, participants
were medically evaluated during and for 1 week after
drug administration.
At 0900 on the day of testing, participants received a
single oral dose of placebo (n 10) or up to 90 mg of
rimonabant. Two hours later, they smoked a cannabis
cigarette containing 2.64% THC by weight, estimated to
contain 20 mg of THC. The number of puffs and time
between puffs were standardized. Blood for THC and
THCCOOH assays was drawn from an indwelling venous
catheter in the arm 120 and 5 min before cannabis
smoking and at 2, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110, and
235 min after the start of smoking. Heparinized plasma
was stored at 20 °C until analysis. Four hours after
smoking the first cigarette, 30 of the men smoked a second
cigarette containing 2.64% THC. Blood samples were
collected at the same time intervals as described for the
first dose, except that the 235-min collection was omitted.
The previously published study designed to investigate
the blockade of cannabis effects demonstrated that
rimonabant had no effect on THC and THCCOOH pharmacokinetics
(24 ). The participants receiving rimonabant
and placebo could therefore be grouped together for the
current study and the results generalized to cannabis
users.
Plasma THC and THCCOOH concentrations were determined
in plasma from blood collected from 38 participants
in the morning smoking session and 30 who also
smoked in the afternoon. Occasional samples had THCCOOH
2.5 g/L, the limit of quantification (LOQ) for
the method. In all, 717 plasma THC and 704 plasma
THCCOOH concentrations were evaluated with the two
predictive models.
analytical method
Plasma samples were analyzed for THC and THCCOOH
by a previously published method (25 ). Briefly, trideuterated
THC and THCCOOH internal standards were added
to plasma to improve identification and quantification.
Proteins were precipitated with acetonitrile. The extraction
method used CleanScreen solid-phase extraction columns
(United Chemical Technologies), with THC eluted
with hexane—ethyl acetate—ammonia hydroxide (93
2 byvolume) and THCCOOH eluted with hexane—ethyl acetate
(70:30 by volume). Trifluoroacetyl-THC and trifluoroacetyl-
hexafluoroisopropyl-THCCOOH derivatives in the
same reconstituted extract were injected into a Finnegan/
MAT Model 4023 gas chromatograph—mass spectrometer
operated in the negative-ion chemical ionization mode
with methane as reagent gas, helium as carrier gas, and a
DB 1 capillary column (J & W Scientific). Cannabinoid
concentrations were determined by use of ion m/z 410 for
THC and m/z 422 for THCCOOH. The LOQs were 0.5
g/L for THC and 2.5 g/L for THCCOOH, with CVs
across the analytical range of 4.1%—11% and 4.9%—12%,
respectively.
predictive models
Huestis et al. (19 ) previously published models for estimating
time of last cannabis use. Model I determined time
estimates from plasma THC concentrations and model II
from the plasma THCCOOH/THC concentration ratios.
The formulas are reproduced below, with t representing
the elapsed time in hours between the beginning of
cannabis smoking and blood collection, and CI representing
the 95% confidence interval for the estimate of t. The
subscripts 1 and 2 refer to models I and II, respectively,
and brackets indicate the concentrations of THC or THCCOOH
in g/L:Model I: log t 0.698 log [THC] 0.687
log CI1 log t 1.975
0.0301.006
(log[THC] 0.996)2
89.937
Model II: log t (0.576 log [THCCOOH]/[THC]) 0.176
log CI2 log t 1.975
0.045 1.006
(log[THCCOOH]/[THC] 0.283)2
123.420
These equations were developed from cannabinoid
concentrations in plasma samples collected for up to 168 h
after controlled smoking of a 1.75% and a 3.55% THC
cigarette by each of 6 cannabis users residing continuously
in a secure clinical research unit (19 ). Drug administration
was not initiated until their urine cannabinoid
concentrations were 20 g/L. Table 1 is reproduced
from that report (19 ) and displays the 95% CIs, in hours,
for selected predicted times after smoking using models I
and II. These results are presented to show that the
models predict last use within an interval of time. The
magnitude of the interval is smaller when the elapsed
time between cannabis use and blood collection is shorter.
In the current study, model I was applied to each
plasma sample with a valid THC measurement and
model II to those with valid THC and THCCOOH concentrations.
All analyte results at or above the LOQs of the
method were selected. For each model, the CI for time of
last use was determined. The actual times of smoking
were examined to determine whether they were within
(i.e., correct) or outside (i.e., incorrect) the CI. The following
parameters were calculated for each model: (a) accuracy
[(number of correct time estimates/total number of
estimates) 100%]; (b) the mean magnitude of error
(mean of absolute values of tactual tcalculated for all
incorrect estimates); and (c) the number of samples with
actual times greater than the upper limit of the CI (i.e., the
model estimated a shorter t than actual, termed an underestimate)
or below the lower limit (i.e., the model estimated
a longer t than actual, termed an overestimate) for
each situation examined.
We applied the same evaluations to each sample, using
the models in combination. With this application, estimates
were considered correct if the true value of t fell
within a range defined by the lower limit of the CI of
either model and the upper limits of the CIs, whichever
was highest.
Results
The results for predicted elapsed times between the
beginning of cannabis smoking and blood collection obtained
with model I, model II, and a combination of the
models are summarized in Table 2. The conditions for the
single cigarette study were similar to those in the study
used to produce the models, except that cigarettes contained
2.64% THC instead of either 1.75% or 3.55% THC,
and length of inhalation and the time smoke was held in
the lungs were not controlled. For model I, Table 2 reflects
that, for 392 of 427 samples collected from 38 individuals
after they began smoking the first cigarette, the observed
time fell within the predicted range of elapsed time.
Another way to view this result is that the model was
correct for 91.8% of cases. For those cases that were
overestimated, the average error of actual elapsed times
was 14 min lower than the lowest value in the CI. There
were no underestimates when model I was applied to
data obtained after the first cigarette.
As can also be seen in Table 2, the models provided
similar results after volunteers (n 30) smoked a second
cannabis cigarette in the afternoon. A difference noted
after multiple cigarettes was that 3 of 290 samples had
actual elapsed times longer than predicted by model I, i.e.,
for these 3 samples the time of last use was underestimated.
Model II correctly estimated the time of last smoked
dose for 94.0% of samples after a single cigarette. Unlike
model I, most of the errors (21 of 415 samples) were
underestimates. Estimates were more accurate after multiple
doses.
We evaluated whether the CIs for models I and II could
be combined to produce superior results. Shown in the
lower portion of Table 2 are the accuracy, overestimates,
and underestimates in predicted elapsed times when we
used models I and II in combination. If the actual elapsed
time after the beginning of smoking fell within a range
with a lower limit equal to the lowest predicted confidence
limit for either model and an upper limit equal to
the highest predicted confidence limit for either model,
then results were considered correct. Accuracies were
near 99%, and there were no instances of underestimated
predictions.
One of the limitations mentioned in the introduction
was that the models had not been challenged for plasma
THC concentrations 2 g/L. This is an important limitation
because concentrations in this range are expected in
the first 4 h after cannabis smoking, the timeframe in
which many individuals are impaired and attempt to
perform skilled operations (20 ). In this study, 77 plasma
samples from 26 individuals had THC concentrations in
the range of 0.5—2 g/L. The results of application of the
models to these samples are shown in Table 3. All valid
predictions were accurate with the combination of models
I and II. Independently, the models were not as accurate
for these concentrations as they were for samples with
concentrations 2 g/L: 80.5% compared with 91.2% for
model I, and 77.6% compared with 95.5% for model II.
Discussion
For most forensic and medical applications, the ultimate
goal of determining the time of last cannabis use is to
relate this time to effects of the drug on the body. Effects
vary among individuals, but most people experience the
physiologic effects of increased heart rate and conjuncti-val injection, or bloodshot eyes (26, 27). These effects
begin during smoking and may last up to several hours.
Effects on the brain include euphoria and decrements in
memory, the ability to maintain attention, estimating
time, and coordination on divided attention tasks (23 ).
The length of time that acute effects on the brain continue
has been debated among scientists. Some investigators
have reported subjective and impaired behavioral effects
for up to 3 h after the end of smoking but could not find
significant effects the following day (7, 28—30). Others
have reported decrements in cognitive function and complex
performance tasks for more than 24 h (31—33 ). A
consensus panel of experts that met to determine the
current state of knowledge for effects of cannabis on
driving performance reported that "most behavioral and
physiological effects return to baseline levels within 3—5 h
after drug use, although some investigators have demonstrated
residual effects in specific behaviors such as complex
divided attention tasks for up to 24 h" (23 ). Using the
models and an individual's plasma THC and THCCOOH
concentrations, one may estimate the time the individual
last used cannabis and from this time predict cognitive or
performance decrements.
The results of this study demonstrate that model I
accurately predicts the time of last cannabis use for 90%
of cases and that incorrect predictions are overestimates
after single doses. After multiple doses of cannabis, accuracy
remains high, but 3 of 290 results were underestimated.
Although that number is small, we pay special
attention to these underestimates because in general they
have a greater impact on actual cases than do overestimates.
For example, if a judicial investigator or physician
wants to know whether a person with THC in his or her
blood was impaired at the time of an accident, it is
important to know how long before the accident that
person smoked cannabis. Underestimating this time increases
the potential for falsely accusing an individual of
being impaired at the time of the accident. For our 3 cases,
the underestimates were not serious problems, however,
because the magnitudes were small, ranging from 2 to 11
min. If we used the estimated times in these cases to
predict the effects of cannabis on the individuals, the
errors in time are small compared with the variability in
specific effects and will not impact our decision.
Determining accuracy after a second cigarette is an
important finding because previous studies did not address
applying the models to multiple THC administrations.
Bogusz, in a letter to the editor in the Journal of
Analytical Toxicology (34 ), questioned the applicability of
the predictive models when individuals ingested multiple
doses of cannabis. Huestis and Cone addressed many, but
not all, of his concerns in a response and suggested that
the models be examined in a multiple-dose study (34 ). We
now demonstrate that model I gives accurate predictions
for 90% of plasma samples collected after multiple
cannabis doses.
After single doses, model II was 94.0% accurate, but
most errors (21 of 415 samples) were underestimates.
However, all 21 were from samples collected 3 h after
smoking. Huestis et al. (19 ) noted in the publication of the
models that predictions at longer elapsed times were
occasionally underestimated. For those times that were
underestimated, the magnitude ranged from 2 to 94 min
with a mean of 38 min.
The combined models gave time estimates that were
99.1% accurate with no underestimates. In addition, when
overestimates occurred, they were less than 4 min. For the
subset of samples with THC concentrations between 0.5
and 2.0 g/L and THCCOOH concentrations 2.5 g/L,
the combined-model approach was correct in all instances.
This method of analysis yields a longer estimated
time interval, but greater certainty in prediction. One
important outcome of greater certainty is that underestimates
of time of use were eliminated. For forensic cases in
which finders of fact wish to avoid underestimating time
of use, the combined CI model offers greater accuracy and
is the method of choice.
The cannabis cigarettes used in our study contained
2.64% THC. Individuals smoked 1 puff/min but chose
their own depth of inhalation. This rate is greater than
self-paced smoking. All had increases in heart rate and
subjective drug effects. According to ElSohly et al. (35 ), in
1997 the mean THC concentration in seized cannabis was
4.2%. Of course, cannabis can have a higher THC content.
The models were developed from plasma data collected
after persons smoked a 1.75% and a 3.55% cigarette, the
latter having more THC than used in the current study
(19 ). Although peak concentrations were higher for the
3.55% cigarettes, the times of use fell within a range
similar to that for the lower-dose cigarette. Other studies
also have shown that cannabis users tend to titrate their
dose of drug to maintain the level of intoxication they
prefer (20 ). One might expect users who are free to choose
their own smoking pattern to keep the dose of THC
within a range that produces this effect even if the
cigarette THC content is high. Their plasma THC and
THCCOOH concentration profiles would be similar to
those after smoking of a less potent cigarette. In addition,
the models have been shown to be accurate in many legal
proceedings in multiple countries over the last 13 years
when the times of cannabis use were known.
A common problem in applying the models to actual
situations is that many accident victims die and THC and
THCCOOH concentrations are available only for postmortem
blood. As mentioned in the introduction, the
average antemortem blood-to-plasma ratio is 0.5. One
can estimate a plasma concentration from a blood concentration
by dividing by 0.5 and inserting the plasma
concentration into the formulas to estimate time of last
cannabis use. However, in fatal accident cases, the important
variables are the concentrations of drugs and metabolites
in antemortem plasma; estimating these from postmortem
blood has not been well documented. Giroud et
al. (36 ) studied the concentrations of THC and THCCOOH
in blood and plasma from live patients and found
mean ratios similar to that reported above, specifically
0.67 and 0.59, respectively. These authors also measured
postmortem blood and postmortem "serum" (obtained
from centrifuging postmortem whole blood) cannabinoid
concentrations and found variable ratios with mean
blood-to-serum ratios of 0.45 for THC and 0.37 for THCCOOH.
The causes of this variability can be many, including
changes in serum after death and postmortem redistribution.
The study did have an unavoidable limitation of
being unable to relate postmortem blood and antemortem
plasma analyte concentrations. Errors in converting postmortem
blood to antemortem plasma concentrations may
have an impact on predictions using model I but would
have less effect on predictions using model II because
model II estimates are based on the ratio of THCCOOH to
THC. These 2 cannabinoid molecules are similarly partitioned
between plasma and blood cells, making the ratio
less sensitive to partition variability.
In conclusion, in this study we extended the validation of
2 previously described models for predicting the time of
last cannabis use from plasma THC and THCCOOH
concentrations to samples collected after multiple cannabis
exposures and also those with low THC concentrations.
The predictive models provide an objective and
validated method for assessing the contribution of cannabis
to accidents or clinical symptoms based on cannabinoid
concentrations in a single plasma sample.
This research was supported in part by the Intramural
Research Program of the NIH, NIDA, and by Sanofi-
Aventis. We thank D.A. Gorelick, S.J. Heishman, K.L.
Preston, R.A. Nelson, and E.T. Moolchan, coinvestigators
on the study in which plasma cannabinoid data were
collected.
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Source: Estimating the Time of Last Cannabis Use from Plasma {Delta}9-Tetrahydrocannabinol and 11-nor-9-Carboxy-{Delta}9-Tetrahydrocannabinol Concentrations
