nimbios conference presentation2_jilane
TRANSCRIPT
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Mathematical Modeling of Cannabinoid
Pharmacokinetics
School of Chemical Engineering
Oklahoma State University
Jacquelyn I. Lane
Ashlee N. Ford Versypt, Ph.D.
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Acknowledgments
2
Research team:
Dr. Ashlee Ford Versypt,
Minu Pilvankar,
Alexandra McPeak,
Jonathan Ramos, Kody
Harper, Michele Higgins,
Grace Harrell, Anya
Zornes, Ye Nguyen
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High levels of cannabis are proven to impair
driving ability, implying a public safety risk
3
Drug test results were among drivers tested.Traffic Safety Facts. 2010
In 2009, 1 in 3
drivers tested
positive for drugs
2XTHC presence in the
blood doubles the
likelihood of a fatal
car accident
Wilson FA, Stimpson JP, Pagán JA. (2014)Biecheler M-B, Peytavin J-F, Facy F, Martineau H. (2008)
Elvik R. (2013)
12.6%
1.5%
Cannabis Alcohol
US Weekend
Nighttime Drivers
Berning, A., Compton, R., Wochinger, K. 2013-2014 National Roadside Survey of alcohol and
drug use by drivers. 2015.
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The main psychoactive ingredient, THC, is fat
soluble, making cannabinoid levels difficult to
quantify
4Ashton, C. H. British Journal of Psychiatry, 2001
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The main psychoactive ingredient, THC, is fat
soluble, making cannabinoid levels difficult to
quantify
5Ashton, C. H. British Journal of Psychiatry, 2001
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The main psychoactive ingredient, THC, is fat
soluble, making cannabinoid levels difficult to
quantify
6Ashton, C. H. British Journal of Psychiatry, 2001
Current tests:
• Urine
• Hair follicle
• Blood
concentration
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Two models are utilized for this study:
a forward model and a reverse model
7
Forward Model:System of ODEs
Time and
method of
dosage
THC blood
concentration
Reverse Model:
Curve-fitting to
create predictive
function
Time since
last dosage
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This 4-compartment model is utilized as a
surrogate for experimental studies
8Heuberger, J. A., Guan, Z., Oyetayo, O. Clinical Pharmacokinetics, 2014
Forward Model
A1 A2
A3
A4
Ka
K23
K32
K24
K42
K20
Ora
l (F1
)
IV (
F=1
00
%)
Inh
ale
(F2
)A1=stomachA2=blood plasmaA3=fatty tissuesA4=brain
F1=oral bioavailabilityF2=inhalation bioavailability
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Step 1: Utilize the 4-compartment forward
model to get THC blood concentration data
9Heuberger, J. A., Guan, Z., Oyetayo, O. Clinical Pharmacokinetics, 2014
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Step 1: Utilize the 4-compartment forward
model to get THC blood concentration data
10Heuberger, J. A., Guan, Z., Oyetayo, O. Clinical Pharmacokinetics, 2014
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Step 1: Utilize the 4-compartment forward
model to get THC blood concentration data
11Heuberger, J. A., Guan, Z., Oyetayo, O. Clinical Pharmacokinetics, 2014
Let’s take a chronic user smoking a single cannabis cigarette after several days of abstinence for example
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Step 1: Utilize the 4-compartment forward
model to get THC blood concentration data
12Heuberger, J. A., Guan, Z., Oyetayo, O. Clinical Pharmacokinetics, 2014
Let’s take a chronic user smoking a single cannabis cigarette after several days of abstinence for example
\ \
\ \
Key Assumptions:• Dose size = 40-60 mg/cigarette• Bioavailability = 11%• Time to smoke = 5–10 mins• Volume of blood = 6 L
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Using data for a chronic cannabis user smoking
a single cannabis cigarette, we created the
following THC concentration curves
13
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Using data for a chronic cannabis user smoking
a single cannabis cigarette, we created the
following THC concentration curves
14
Blood plasma
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Using data for a chronic cannabis user smoking
a single cannabis cigarette, we created the
following THC concentration curves
15
Blood plasma
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Step 2: Utilize MATLAB lsqcurvefit to find a
mathematical model to fit the concentration data
16
Reverse Model
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Step 2: Utilize MATLAB lsqcurvefit to find a
mathematical model to fit the concentration data
17
For 10 min to smoke:
coef(1)=-0.5943coef(2)=1.3336
Resnorm=34.6
Reverse Model
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As dose size and time of dosage (time to smoke)
are varied, the modeled coefficients also vary
18
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As dose size and time of dosage (time to smoke)
are varied, the modeled coefficients also vary
19
Coef(2) affected more by dose size
Coef(1) affected more by dose time
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As dose size and time of dosage (time to smoke)
are varied, the modeled coefficients also vary
20
Coef(2) affected more by dose size
Coef(1) affected more by dose time
Reverse model is more accurate over longer dosing intervals
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21
Reverse model
Forward model
Conclusion: Developed a model capable of
predicting time of last dosage for inhalation of a
single cannabis cigarette
Series of ODEs to find THC blood concentration given route of dosage and time
Mathematical model to predict time of last dosage
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Advantages• Forward model serves as a surrogate for
experimental testing and can take into
account multiple routes of dosage
22
Reverse model
Forward model
Conclusion: Developed a model capable of
predicting time of last dosage for inhalation of a
single cannabis cigarette
Series of ODEs to find THC blood concentration given route of dosage and time
Mathematical model to predict time of last dosage
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Advantages• Forward model serves as a surrogate for
experimental testing and can take into
account multiple routes of dosage
• Reverse model for a single dosage is very
accurate
23
Reverse model
Forward model
Conclusion: Developed a model capable of
predicting time of last dosage for inhalation of a
single cannabis cigarette
Series of ODEs to find THC blood concentration given route of dosage and time
Mathematical model to predict time of last dosage
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Advantages• Forward model serves as a surrogate for
experimental testing and can take into
account multiple routes of dosage
• Reverse model for a single dosage is very
accurate
• More accurately models cannabis
consumption than positive/negative tests24
Reverse model
Forward model
Conclusion: Developed a model capable of
predicting time of last dosage for inhalation of a
single cannabis cigarette
Series of ODEs to find THC blood concentration given route of dosage and time
Mathematical model to predict time of last dosage
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Advantages• Forward model serves as a surrogate for
experimental testing and can take into
account multiple routes of dosage
• Reverse model for a single dosage is very
accurate
• More accurately models cannabis
consumption than positive/negative tests
Future Work• Develop a model for each
route of dosage and combine into a single framework
• Expand model to include ad libitum cannabis users
25
Reverse model
Forward model
Conclusion: Developed a model capable of
predicting time of last dosage for inhalation of a
single cannabis cigarette
Series of ODEs to find THC blood concentration given route of dosage and time
Mathematical model to predict time of last dosage
![Page 26: Nimbios conference presentation2_JILane](https://reader034.vdocuments.us/reader034/viewer/2022051523/588267271a28ab470c8b4b1f/html5/thumbnails/26.jpg)
Advantages• Forward model serves as a surrogate for
experimental testing and can take into
account multiple routes of dosage
• Reverse model for a single dosage is very
accurate
• More accurately models cannabis
consumption than positive/negative tests
Future Work• Develop a model for each
route of dosage and combine into a single framework
• Expand model to include ad libitum cannabis users
26Questions?
Reverse model
Forward model
Conclusion: Developed a model capable of
predicting time of last dosage for inhalation of a
single cannabis cigarette
Series of ODEs to find THC blood concentration given route of dosage and time
Mathematical model to predict time of last dosage
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This 4-compartment model is utilized as a
surrogate for experimental studies
28
Heuberger, J. A., Guan, Z., Oyetayo, O. Clinical Pharmacokinetics, 2014
Forward Model