PHARMACOKINETIC AND PHARMACODYNAMIC CHARACTERIZATION OF TETRAHYDROCANNABINOL INDUCED CANNABINOID DEPENDENCE IN RATS

By

ABHIGYAN RAVULA

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

© 2018 Abhigyan Ravula

This work is dedicated to my parents, brother and family for their love and support

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ACKNOWLEDGMENTS

I would like to express my sincere gratitude to my research advisor Dr. Hartmut

Derendorf for accepting me into his research group and for his guidance and support

during my PhD training at University of Florida. I would also like to thank my committee

members Drs. Hartmut Derendorf, Adriaan Bruijnzeel, Shiong Song and Maureen Long

for their never-ending support during the course of the program. I am also thankful to

Drs. Barry Setlow and Marcelo Febo for giving me the opportunity to work on the

cannabis smoke exposure project that helped me shape my thesis. I would also like to

thank the office staff in the Department of Pharmaceutics for their constant support.

I am indebted to the Department of Pharmaceutics, Department of Psychiatry,

University of Florida Cricket Club and the University of Florida for my personal and

professional development.

I would like to express special appreciation to Drs. Li Li and Tong Shen for giving

me an opportunity to intern at Eli Lilly and which had a profound impact on the

development of my scientific acuity.

I would like to extend special thanks to the post-docs and my lab mates,

especially Drs. Hardik Chandasana and Satyawan Jadhav and Vipada

Khaowroongrueng and my fellow graduate students Drs. Tobias Heinrichs, Ameila

Deitchman, Johannes Kast and Wendy (Yu Yichao) and Abhinav Kurumaddali.

I am also very grateful to Dr. Shashank Rohatagi for his invaluable advices and

recommendation of this wonderful platform to build a successful career.

I would also like to thank my friends and University of Florida Cricket Club for

making my stay in Gainesville special and memorable. Finally, I would like to thank my

family from the bottom of my heart for their constant support, guidance and patience.

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TABLE OF CONTENTS Page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 11

ABSTRACT ................................................................................................................... 12

CHAPTER

1 INTRODUCTION .................................................................................................... 14

Clinical Pharmacokinetic and Pharmacodynamics of Tetrahydrocannabinol .......... 14

Government Regulation in United States ................................................................ 15 Pharmacokinetics of THC ....................................................................................... 16

Absorption ........................................................................................................ 16

Distribution ....................................................................................................... 19 Metabolism ....................................................................................................... 20

Elimination ........................................................................................................ 21 Pharmacodynamics of THC .................................................................................... 22 Discussion .............................................................................................................. 26

2 LC-MS/MS METHOD DEVELOPMENT AND VALIDATION ................................... 28

Background ............................................................................................................. 28 Materials and Methods............................................................................................ 30

Chemical and Reagents ................................................................................... 30

Instrumentation and Chromatographic Conditions ........................................... 30 Stock and Standard Quality Controls ............................................................... 31 Extraction Procedure ........................................................................................ 31

Assay Validation Procedure ............................................................................. 32 Selectivity and sensitivity ........................................................................... 32 Stability and recovery ................................................................................. 32 Intra-day and inter-day accuracy and precision ......................................... 33

Matrix effect ............................................................................................... 33 Results .................................................................................................................... 33

Method Development ....................................................................................... 33

Method Validation ............................................................................................. 34 Selectivity ................................................................................................... 34 Calibration curve and linearity .................................................................... 34 Extraction efficiency and matrix effect ........................................................ 34 Accuracy, precision and stability ................................................................ 34

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Summary ................................................................................................................ 35

3 PHARMACOKINETICS OF TETRAHYDROCANNABINOL IN RATS ..................... 41

Background ............................................................................................................. 41 Materials and Methods............................................................................................ 42

Subjects............................................................................................................ 42 Drugs ................................................................................................................ 42 Drug Analysis ................................................................................................... 42

Experimental Design ........................................................................................ 43 Dose Metrics ........................................................................................................... 43 Data Analysis .......................................................................................................... 45 Results .................................................................................................................... 47

Non-Compartment Analysis of THC ................................................................. 47

Non-Compartment Analysis of THC when Co-administered with Rimonabant ................................................................................................... 47

Compartment Modelling for THC ............................................................... 48 Compartment Modeling for Rimonabant .................................................... 48

Discussion .............................................................................................................. 49

4 CHARACTERIZATION OF CANNABINOID ABUSE AND WITHDRAWAL............. 65

Cannabiniod Abuse ................................................................................................ 65

Materials and Methods............................................................................................ 67 Subjects............................................................................................................ 67

Drugs ................................................................................................................ 67 Cannabis Smoke Exposure .............................................................................. 68

ICSS Apparatus ................................................................................................ 68 Experimental Design ........................................................................................ 69

Blood Collection ............................................................................................... 69 Drug Analysis ................................................................................................... 70 Statistical Analysis ............................................................................................ 70

Intracranial Self Stimulation .............................................................................. 70 Results .................................................................................................................... 71

Effect of THC Dose and Duration on Baseline Reward Threshold ................... 71

Effect of Rimonabant Induced Precipitated Withdrawal on Brain Reward Threshold ...................................................................................................... 72

Discussion .............................................................................................................. 73

5 PHARMACOKINETIC AND PHARMACODYNAMIC MODELIING OF THC INDUCED CANNABINOID DEPENDENCE ............................................................ 82

Pharmacokinetic and Pharmacodynamic (PK/PD) Modeling .................................. 82 Methods .................................................................................................................. 84

PK/PD modeling of THC for animals which were exposed to cannabis smoke containing THC .................................................................................................... 84

PD Data Smoothing ................................................................................................ 85

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There compartment PK with-effect-compartment indirect-link model (biophase equilibration model with a linear PD model) ......................................................... 86

There compartment PK with indirect response model using stimulation of the output (IDR-4) ...................................................................................................... 87

Results .................................................................................................................... 89 Discussion .............................................................................................................. 89

6 CONCLUSION ........................................................................................................ 99

LIST OF REFERENCES ............................................................................................. 101

BIOGRAPHICAL SKETCH .......................................................................................... 110

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LIST OF TABLES

Table page 1-1 Clinical Studies to characterize the PK-PD of THC using various routes of

Administration (1980-2014) ................................................................................ 27

2-1 Optimized source and compound dependent parameters for THC, CBD, Rimonabant and THC-D3 ................................................................................... 36

2-2 Accuracy and Precision for within batch and between QC samples .................. 37

2-3 Mean extraction recovery of analytes in rat plasma ........................................... 38

2-4 Mean Stability of analytes in rat plasma ............................................................ 38

3-1 Phamramcokinetic Parameters of THC in rats reported in literature .................. 51

3-2 Phamramcokinetic Parameters obtained after performing NCA using Phoenix8.0.......................................................................................................... 51

3-3 Pharmacokinetic Parameters obtained after performing NCA using when Rimonabant was co-administered with THC ....................................................... 51

3-4 Phamramcokinetic Parameters of THC upon co-administration with Rimonabant obtained after performing NCA using Phoenix8.0 .......................... 51

3-5 Phamramcokinetic Parameters obtained after performing compartmental analysis for THC ................................................................................................. 52

3-6 Phamramcokinetic Parameters obtained after performing compartmental analysis for Rimonabant ..................................................................................... 52

3-7 Nicotine Chamber concentration vs TSP ............................................................ 53

4-1 Summary of the total amount of cannabis burnt for both the treatments,. ......... 81

5-1 Summary ok the PD parameters obtained after model fitting ............................. 98

9

LIST OF FIGURES

Figure page 2-1 Figure Structures of THC, CBD and Rimonabant Chemical structures of (a)

∆9-tetrahydrocannabinol (b) cannabidiol and (c) rimonabant. ............................. 39

2-2 Representative Chromatograms for the compounds .......................................... 40

3-1 One Compartment Pharmacokinetic Model ........................................................ 54

3-2 Two Compartment Pharmacokinetic Model ........................................................ 54

3-3 Three compartment pharmacokinetic model ...................................................... 55

3-4 TSP (mg/m3) vs Nicotine (ug/L) ......................................................................... 55

3-5 Plasma Concentration of THC after Intravenous Administration......................... 56

3-6 Plasma Concentration of THC after passive smoke inhalation (0.05 mg/kg) ...... 57

3-7 Plasma Concentration of THC after passive smoke inhalation (0.2 mg/kg) ........ 58

3-8 Plasma Concentration of THC after passive smoke inhalation in presence of Rimonabant (0.05 mg/kg) ................................................................................... 59

3-9 Plasma Concentration of Rimonabant (3 mg/kg) after smoke exposure ............ 60

3-10 Goodness of fit plots for THC PK model ............................................................. 61

3-11 VPC for THC PK model ...................................................................................... 61

3-12 Individual profiles for subject administered THC ................................................. 62

3-13 Individual profiles for subject administered rimonabant (i.p) ............................... 63

3-14 GOF’s rimonabant (i.p) ....................................................................................... 64

3-15 VPC rimonabant (i.p) .......................................................................................... 64

4-1 ICSS Set Up ....................................................................................................... 77

4-2 Experimental design ........................................................................................... 78

4-3 Experimental design-2 ........................................................................................ 78

4-4 Effect of THC Dose and Duration of Exposure on Brain Reward Threshold ....... 79

4-5 Effect of Rimonabant (i.p) on Brain reward Thresholds ...................................... 80

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5-1 Placebo corrected PD response ......................................................................... 94

5-2 PK-PD model using hypothetical effect compartment ......................................... 95

5-3 PK-PD model using indirect response model (IDR4) .......................................... 95

5-4 Linear Emax model ............................................................................................. 96

5-5 Observed vs model prediction for THC induced cannabinoid dependence ........ 96

5-6 GOF’s PD Model ................................................................................................ 97

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LIST OF ABBREVIATIONS

ACN Acetonitrile

CBD Cannabidiol

CE Collision energy

CXP Collision exit potential

DP Declustering potential

DSM The Diagnostic and Statistical Manual of Mental Disorders

EC50 Concentration producing half maximal effect

Emax Maximum effect

EP Entrance potential

GOF Goodness of fit

ICSS Intracranial self-stimulation

LC Liquid chromatography

MEOH Methanol

MS Mass spectrometry

PD Pharmacodynamics

PK Pharmacokinetics

THC Tetrahydrocannabinol

USFDA/FDA United States Food Drug Administration

VPC Visual Predictive check

WHO World Health Organization

ℽ Hill- coefficient

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

PHARMACOKINETIC AND PHARMACODYNAMIC CHARACTERIZATION OF

TETRAHYDROCANNABINOL INDUCED CANNABINOID DEPENDENCE IN RATS

By

Abhigyan Ravula

May 2018

Chair: Hartmut Derendorf Cochair: Adriaan Bruijnzeel Major: Pharmaceutical Sciences - Pharmacy

2.5% of earth’s population uses marijuana, making it the most commonly abused

recreational drug around globe. Delta-9-Tetrahydrocannabinol (THC) was recognized as

the active constituent, and the effects of THC have been well studied. Cannabinoids

have a vast variety of therapeutic applications such as in pain, nausea, vomiting,

appetite stimulant, however therapeutic advantages of cannabis is being counteracted

by its abuse potential, hence limiting its clinical application.

The aims of these studies were to verify and characterize the correlations among

tetrahydrocannabinol pharmacokinetics, physical dependence and withdrawal. We

intended to explore exposure-response relationships and to understand the underlying

mechanism of tolerance and withdrawal upon cannabis smoke inhalation as this is most

preferred way to consume cannabis in humans.

In order to study the concentration-effect relationship, an LCMS assay to quantify

THC in rat plasma was developed, animal experiments were chosen and conducted in

lab. Three doses (0.05, 0.2 and 1 mg/kg) of THC were tested through passive smoke

inhalation and intravenous administration for pharmacokinetic (PK) assessment. In the

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dependence studies, two doses (0.05 and 0.2 mg/kg/day) of THC smoke or air only

exposure was performed for a total of four weeks to understand the effects after chronic

passive smoke exposure. A discrete-trial intracranial self-stimulation procedure was

used to quantify the extent of dependence and withdrawal.

The assay to quantify THC, CBD and rimonabant was developed and validated

according FDA guidelines.

The PK of THC could be well described with a linear three-compartment model.

For dependence studies, THC induced physical dependence which was CB1 receptor

mediated was verified after passive smoke inhalation and was characterized by PK/PD

models.

Results from these studies shows that exposure to even very low doses of

cannabis smoke over prolonged time can induce dependence. They provide insight into

the underlying mechanism of physical dependence. These animal models will open a

new therapeutic interest in the treatment of cannabis use disorders.

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CHAPTER 1 INTRODUCTION

Clinical Pharmacokinetic and Pharmacodynamics of Tetrahydrocannabinol

Cannabis is one of the most commonly abused illicit drug in the United States

and around the world. According to WHO reports about 2.5% of earth’s population are

marijuana users, which is surprisingly high in contrast to cocaine (0.2%) and other

opiates (0.2%)(“WHO | Cannabis,” 2010). At the moment there are ongoing efforts for

legalization of marijuana in the United States and 6 states have legalized it for

recreational purpose, but it is still considered a schedule 1 substance under the federal

law. Cannabis is most commonly used a marijuana or hashish with the former being

dried leaves and flowers of the plant and later which is a resin that is extracted from

plant material. Prevalence and use of synthetic cannabis (spice) has increased over the

last decade(Panlilio et al., 2015). The cannabis plant contains more than 500

compounds of which at least 113 belong to the cannabinoids. Marijuana is commonly

administered orally or by inhalation after vaporizing the drug. Delta-9-

tetrahydrocannabinol (THC) was identified as the principal psychoactive constituent and

the effects of THC have been well studied. Cannabidiol (CBD) was identified as a non-

psychoactive constituent with a variety of therapeutic applications. Work is also going

on to understand the potential benefits of CBD due to the absence of psychotropic

effects(Niesink and van Laar, 2013). Other Cannabinoids present in the plant may have

different effects compared to pure THC and how they modulate the effect of THC is yet

to be fully explored. The psychotropic effects of THC are mediated through the

cannabinoid receptors (CBR). CBR’s function by inhibition of adenylyl cyclase and a

part of the G-protein receptors. THC is a partial agonist for the CB1 and CB2 receptor

15

and also acts on PPARy and GPR55 receptors, with the later contributing to the anti-

nociceptive effects(Izzo et al., 2009). Along with CBR’s, CBD acts on U-opioid, GRP55,

PPARy and Transient receptor potential (TRP) receptors. CBD has anxiolytic,

neuroprotective effects and can also antagonize the psychotropic effects of

THC(Niesink and van Laar, 2013)

Government Regulation in United States

Cannabis is an age old herb that has been known to have therapeutic value

throughout history. Some of the Indian and Chinese literature dates the use of this herb

almost 2000 years back in the history. By mid-1800’s the therapeutic potential of

cannabis and derived products was recognized and it was included in the United States

Pharmacopoeia from 1850 to 1941 for its medicinal value. The use of these products

had started to deplete with development of new chemicals (aspirin, opioids) for

treatment of pain and other conditions, leading to regulation of marijuana in 48 states by

1938. One of the first acts by the federal government was in 1937 the “Marijuana Tax

Act” which meant, that growing, selling and buying marijuana was taxable. This was

introduced when the ongoing efforts by the state government to bring drug abuse under

control was not fruitful. With the increasing use of marijuana the “Controlled Substance

Act” was introduced in 1970 which officially placed marijuana in the schedule 1

category. From 1970 to this date the U.S Department of Justice Drug Enforcement

Administration considers marijuana a Schedule 1 substance. This means that the use of

the substance and its products is strictly prohibited due to its high abuse potential. This

act does not differentiate the use of marijuana for medical or recreational purpose and

growing marijuana is considered a felony.

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Pharmacokinetics of THC

Absorption

Smoking is one of the fastest and most efficient way of consuming marijuana, as

it is a rapid and efficient method of delivering the drug to the brain. The Lung is a highly

perfused organ and THC can be detected in the blood almost immediately after inhaling

the first puff(Huestis, 2007, 2009; Zuurman et al., 2008). Ohlsson et.al were one of the

earliest to report that peak plasma THC concentration were within 3 min after smoking

and the mean plasma concertation varied from 33-118 µg/L/ml(Ohlsson et al., 1980).

The systemic bioavailability after smoking varied a great deal (2% to 56%)(Huestis,

2005),(Heuberger et al., 2015), because of inter and intra individual differences in

smoking dynamics. Factors such as total puffs, duration of each puff, hold time, volume

inhaled and spacing between each puff will also impact the drug exposure (Huestis,

2009; Lindgren et al., 1981; Ohlsson et al., 1980; Zuurman et al., 2009). Mario et.al

compared the effects after smoking marijuana cigarettes of 3 different potencies

containing 1.32%,1.97%, 2.54% THC under controlled conditions, and concluded that

the cmax (94.3± 11.6 ng/ml, 107.4± 18.0 ng/ml, 155.1± 17.5 ng/ml) of THC was

depended on the amount of THC present in cigarette and the time taken to reach

maximum concentration was 7-8 min after smoking(Perez-Reyes et al., 1982). Further,

it was suggested that there is a direct relationship between concentrations of THC in

oral fulid and plasma, as THC present in the oral cavity would act like a depot

preparation after smoking and THC would be released into the bloodstream over a

period of time (Huestis and Cone, 2004). Another study which compared the kinetics of

THC in occasional vs heavy users concluded that there was no difference between the

groups when upon smoking a cannabis cigarette, but the concentrations of THC when

17

smoking a placebo cigarette were higher in regular users and almost zero (absent) in

case of the occasional users due to accumulation of THC in former group(Toennes et

al., 2008). The Cmax for regular users was higher (7.9-244.8 ug/l) in contrast to

occasional users (11.9-86 ug/l), even after adjusting for their respective initial plasma

THC concentrations. In another study conducted by Zuurman et.al to evaluate the

effects of intrapulmonary administration of increasing doses of THC using a volcano

vaporizer showed that maximum plasma concentration was seen at the highest dose.

The plasma levels reached peak within minutes after the administration with no

absorption delays(Zuurman et al., 2008)

A number of studies were done in order to characterize the disposition of

cannabinoids after oral administration. Thought smoking is an efficient way to consume

cannabis undesirable effects produced by smoke limits this practice in clinic, hence oral

administration is preferred for therapeutic applications of cannabinoids. One of the

earliest studies characterized the disposition of THC after eating a chocolate cookie

containing 20 mg of THC. In spite administering a high amount, the plasma levels

ranged between 4-11 µg/L with a lower systemic availability (6%) in contrast to smoking.

There was a clear delay to reach the maximum levels and this ranged from 1-5 hours

after eating the cookie(Ohlsson et al., 1980). The Vehicle in which THC is administered

also has an impact on the bioavailability of orally administered THC. This was seen in a

study which compared 5 different vehicles, and conclude that greater bioavailability was

seen when THC was administered in sesame oil(Huestis, 2007). Wall et.al compared

the effects of administering 15 mg and 20 mg THC capsules in sesame oil for women

and men respectively, and concluded that there were no sex differences in the kinetics

18

following oral administration. The peak plasma levels were observed at 1.75-2 h

following drug administration, and remained constant for the next 4-5 hours. There was

an increase in the amount of 11-OH-THC following oral administration due to excess

first pass metabolism(Wall et al., 1983). Another study compared the concentration of

THC after acute single oral administration of 10mg THC and cannabis extract containing

10 mg THC and 5.8 mg CBD to check how CBD affected the kinetics and disposition of

THC. The maximum plasma concentration after THC alone and cannabis extract were

3.20 µg/L and 4.05 µg/L respectively(Nadulski et al., 2005). The tmax reported in this

study was about 1 h for both the treatments. The concentrations of the metabolites 11-

OH-THC and THC-COOH were greater than the active drug as seen with oral

administration due to first pass metabolism. Oral bioavailability is mainly governed by

factors like variable absorption depending the formulation (vehicle used), excessive

hepatic first pass metabolism and degradation in the stomach due to acidic

environment. Hence there is generally some variability associated with maximum

plasma levels and time to reach Cmax. Pharmacokinetics of Namisol® a tablet containing

pure THC having a variety of applications was studied using different doses in healthy

volunteers after oral administration. The observed Cmax were dose dependent (4.69

ng/mL) for 8 mg/kgTHC and lower doses reaching lower concentrations in patients were

4.43 ng/mL and2.92 ng/mL for 6.5 mg and 5 mg respectively. The time taken to reach

the maximum concentration was faster (39-56 min) compared to other

formulations(Ahmed et al., 2014). Pharmacokinetics after administering 20 mg

Dronabinol® were reported to have a Cmax that ranged from (4.4-11.0 µg/L) and the tmax

ranged from 1.5-5 hours(Schwilke et al., 2009). This study also showed that there was

19

no increase in the concentration of THC for continuous dosing, but the concentration of

the metabolites increased steadily. No increase in plasma THC concentrations could be

due extensive GI metabolism. The drug Sativex® is a formulation containing equal

proportions of THC and CBD. There was no observed difference in the

pharmacokinetics of THC in studies comparing the plasma concentrations of pure THC

and Sativex suggesting no pharmacokinetic interactions(Karschner et al., 2011)

Distribution

Initial uptake of THC is high in organs like lung, brain and liver which are highly

perfused followed by storage and accumulation in less perfused fatty tissues.

It has been seen that the plasma concentrations of THC upon smoking marijuana

follows a bi-exponential curve with rapid distribution and a high peak within minutes and

slow elimination. This rapid distribution of THC is attributed to lipophilicity and

corresponding logP value of THC was reported to be 5.94-6.97(Thomas et al., 1990)..

The drug concentration in the tissue isn’t affected by specific transport processes or

barriers. It was shown that the amount of THC in present in brain is less than 1%, with

most of it getting retained in the fat after prolonged drug exposure. The ratio was shown

to be 27:1 and 64:1 after 7 and 27 days of exposure respectively(Huestis, 2009). The

volume of distribution of THC at steady state was determined to be about 3.4

l/kg(Huestis, 2005) and it mainly binds to lipoproteins. The permeability of THC-OH in

brain is greater than THC, hence it diffuses faster compared to THC and is also less

protein bound in contrast to THC. Distribution of THC in drug tolerant vs non-tolerant

animals was shown to be similar, with no decrease in the uptake of THC in the brain.

Further it was shown by Hunt and Jones (1980) that the total metabolic clearance

increases and volume of distribution (605 ml -997 and 2.6- 4.6 l/kg) after prolonged oral

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administration of 30 mg of THC every 4 h for 10-12 days. There was a development of

tolerance to the effects, but it could not be explained by pharmacokinetic changes,

suggesting that the underlying mechanism might be a pharmacodynamic adaptation.

According to reports about 10% of pregnant women use cannabis regularly. Although

no prenatal death was observed, there were consequences like bodyweight reduction,

hyperactivity and inattention in the newborns(Little and VanBeveren, 1996; Park et al.,

2004; Syme et al., 2004). THC and metabolites also diffuse across the

placenta(Huestis, 2005). In early pregnancy, the transfer to fetus was high. Due to its

lipophilicity THC is present in breast milk(Perez-Reyes et al., 1982).

Metabolism

An insight into the metabolism of THC is essential as it provides understanding

on how pharmacology of THC can be altered by its metabolites. Preliminary studies

showed that 11-OH-THC formed thorough microsomal hydroxylation, which in turn is

converted to 11-nor-THC-COOH, with the former being an active metabolite. No sex

differences in the metabolism were observed upon oral and intravenous administration,

although the rate of formation of the 11-OH metabolite was higher when THC was

administered orally. The 11-OH-THC/THC ratio was 0.5 or 1:1 upon oral administration

in contrast to 1:10 or 1:20 after intravenous administration(Wall et al., 1983). Bornheim

et.al 1991 further concluded that CYP2C9 was responsible for the formation of active

11-OH metabolite using human liver microsomes. Although this is the major metabolite,

other metabolites like 8β-OH-THC, 8α-OH-THC and epoxyhexadhyrocannabinol were

also formed in trace amounts. CYP3A catalyzes the formation of minor metabolites. The

Oxidation of 11-OH-THC produces THC-COOH which is an inactive metabolite. There

are numerous single nucleotide polymorphisms (SNP) in the CYP2C9, which can alter

21

the metabolism and pharmacokinetics. One study which aimed to compare SNP’s of

CYP2C9 studied the pharmacokinetics of THC after orally administering THC in 43

healthy individuals. The plasma concentrations ranged from 0.52 ug/l to 10 ug/l. The

median was 2.7 ug/l and 6.3 ug/l for the CYP2C9*1 and CYP2C9*3 allele groups

respectively. The apparent clearance (CL/F) ranged from 200 to 4000 l/h. The group

with the highest plasma THC value had the lowest plasma THC-COOH/THC ratio and

reported maximum sedation. The results showed significant differences in the

pharmacokinetics of THC and its metabolites between the SNP groups. They concluded

that CYP2C9 plays a minor role in the formation of 11-OH but rather promotes the

conversion of 11-OH-THC to THC-COOH. The study also tested if other SNP’s like

CYP3A5*`1 vs CYP3A*3 had an impact on the pharmacokinetics of THC but there were

no statistical differences between these groups(Sachse-Seeboth et al., 2009). In a study

that tried to compare the activities of 12 humans recombinant UGT’s towards classic

cannabinoids, it was shown that THC is not a direct substrate for the UGT’s tested.

Responses for THC-OH, THC-COOH, CBN and CBD shown by UGT1A and UGT1B

family isoenzymes were variable. It was seen that THC-OH was specifically

glucoronidated by UGT1A9 and UGT1A10. UGT1A3 and UGT1A1 showed activity

towards THC-COOH. CBN which is formed by degradation of THC was glucoronidated

by UGT1A10, UGT1A7, UGT1A9 and UGT2B7. It can be said that the phase II

metabolism of THC depends on upstream processing including phase I metabolism by

CYP2C9 and CYP3A4(Mazur et al., 2009).

Elimination

About sixty five percent is excreted in the feces as hydroxyl metabolites and

twenty percentis excreted in the urine in the form of conjugated acid metabolites. There

22

are various metabolites which are conjugated with glucuronic acid to increase their

water solubility. Most of the 11-OH-THC is excreted through feces but the THC-COOH

glucuronic acid conjugate is eliminated through urine. The total amount of THC

eliminated unchanged is negligible(Strougo et al., 2008; Zuurman et al., 2008) . The

amount of THC-COOH excreted in the urine was shown to be directly dependent on the

dose of THC.

Pharmacodynamics of THC

It was in the late 1980’s when the first cannabinoid receptor (CB1) was cloned

and the effects of tetrahydrocannabinol were thought to be CB1 mediated. A few years

down the lane, existence of a second cannabinoid receptor (CB2) mainly present in the

immune system cells was unveiled. Most of the psychotropic effects of THC are

hypothesized to be CB1 receptor mediated. This is mainly due to the fact that CB1

receptors are densely located in the brain and the spinal cord regions. The CB1

receptors are located densely in the cerebral cortex, basal ganglia, limbic areas,

thalamus and brainstem regions of the brain. CB1 receptors are also present in the

periphery and exert some of its effects on the heart, urinary and the gastrointestinal

tracts. CB2 receptors are principally present in immune cells like leucocytes, spleen and

tonsils and there is more mRNA present for CB2 than for CB1 in the immune system.

Levels of CB1 and CB2 mRNA in human leucocytes have been shown to vary with cell

type in the following order B cells > natural killer cells > monocytes >

polymorphonuclear neutrophils, CD4+ and CD8+ cells(Ashton and Ashton, 2012; Cone,

1995). Both of the receptors are G-protein coupled receptors and function by the

inhibiting the release of junctional neurotransmitter. Cannabinoid receptor activation

inhibits the conversion of Adenylylcyclase to cyclicAMP, mainly through Gi-coupling

23

proteins. This leads to the suppression of the voltage gated calcium channels and

activation of the potassium channels resulting in hyperpolarization, as a consequence of

which there is a decreased neurotransmitter release. Different cannabinoids show

different types of effects to the cannabinoid receptors. It was shown that THC has

affinity for both, CB1 and CB2 receptor, with more affinity for the former one. It was also

shown that THC can act as an antagonist on the CB2 receptor(Colizzi et al., 2016;

Grotenhermen, 2004). There are also various synthetic cannabinoids which were

developed to be highly CB1 specific. Anandamide which is an endocannabinoid has

greater affinity for CB1 receptor than the CB2 receptor. It has been shown that cannabis

has a variety of properties like sedative, hypnotic, anxiolytic, analgesic, psychedelic,

and effects almost all the body systems. There haven’t been any cases of toxicity by

cannabis after smoking but the FDA is currently concerned over edible cannabis

products(Ashton and Ashton, 2012; Throckmorton, 2016). A few cases have been

reported that cannabis has induced psychosis and hallucinations after oral

consumption. However the amount of cannabis present in the preparations is unknown

and the rationale behind the unwanted effects is hypothesized to be due to the

metabolism of THC to THC-OH which is twice as potent. As mentioned above the

formation of THC-OH is high when THC is administered orally due to first pass effect.

Ernest Dixon in 1899 was the first to show that the dogs which inhaled cannabis smoke

failed to react to pin pricks. Herbal cannabis can be used for both neuropathic pain and

non-neuropathic pain(Lynch and Campbell, 2011). Evidence exists that the analgesic

efficacy of inhaled cannabis smoke can reduce neuropathic pain and there are about

five randomized controlled clinical trials(Savage et al., 2016). Most of the studies which

24

were carried out used Visual Analogue scores in order to access the pain. In general,

there was a decrease in the VAS indicating a reduction in pain perception. The effect

seemed to be dose dependent with the cannabis cigarette containing the highest

percentage of THC showing the maximum effect(Jensen et al., 2015; Manzanares et al.,

2006; Savage et al., 2016). However, uncertainty regarding relative analgesic actions of

different cannabinoids still persists. The two cannabinoids present in highest

concentration are THC and CBD and evidence suggests that they are both potentially

useful as analgesics. This is clinically relevant in that one (THC) has reward like effects

and the other (CBD) does not. A study conducted by Cooper et.al tried to compare the

effect of smoked marijuana and orally administered Dronabinol, in a randomized, well

controlled, double-blind, placebo study. The results show that both smoked cannabis

and Dronabinol produced analgesia and with Dronabinol producing long lasting

analgesic effect. There is also evidence that suggests the use of cannabinoids in the

treatment of spasticity associated with multiple sclerosis and stroke(Iversen, 2003;

Manzanares et al., 2006). The main reason for recreational use of cannabis is because

of the “high” that is induced after consumption of a marijuana cigarette. Cigarettes

containing as low as 2.5 mg THC can produce profound effects(Ashton and Ashton,

2012). This general high is usually described as a pleasant a relaxing effect with a

general decrease in anxiety, alertness and an increasing euphoria and laughter. The

high can be felt within minutes after smoking the first puff and usually lasts for a couple

of hours. The subjective high for whole cannabis products and THC were described to

be similar(Wachtel et al., 2002). The study conducted by Zuurman et al it showed that

THC was able to alter the feeling about external perceptions which is mainly related to

25

the surroundings but had limited effect on the internal perception scale which is more

related to feelings corresponding to unreality or the psychedelic and hallucinogenic

properties of a drug26. The study also showed that there is a decreased alertness with

increasing doses which is a well characterized effect of THC. The study also looked at

the effect of THC on body sway in a similar experimental setting and the effects of THC

on body sway was also dependent with the highest dose causing the maximum body

sway which was in accordance with the previous literature reports(Liguori et al., 2003).

It is not surprising that THC shares this effect on cognition and psychomotor

performance with other classes of centrally acting sedatives like alcohol and

benzodiazipines. It has also been shown that these effects are additive with other CNS

depressants(Ashton and Ashton, 2012). THC can affect learning, memory and impair

psychomotor coordination and the ability to drive(Kelly et al., 1993). In a study by J.G

Ramekers et.al a clear impairment in driving performance was observed after smoking

low and high doses of marijuana cigarettes compared to the placebo42. This impairment

worsened dramatically when alcohol was also consumed with the cigarettes. Such tests

have been tried before comparing the effects of various centrally acting drugs on driving

impairment using various computerized test and the similar conclusions can be made

from the studies(Kelly et al., 1993). It was shown that the heart rate increased with no

effect on blood pressure in a dose related manner (compared to placebo) in humans

after intrapulmonary administration of THC using a volcano vaporizer(Zuurman et al.,

2008). The heart rate decreased and no accumulation was observed when the dose

was escalated. This is consistent with previous results which showed that THC can

induce tachycardia which can be blocked by beta-blockers and can increase the oxygen

26

demand(Grotenhermen, 2004; Perez-Reyes et al., 1982). Tachycardia up to 160

beats/min can be observed and tolerance to this effect can be seen over chronic

use(Ashton and Ashton, 2012). Several previous studies have also reported orthostatic

hypotension and peripheral vasodilation.

Discussion

Since the public interest in the medical use of cannabis has risen sharply in

recent years one aim was to focus on important pharmacokinetic parameters which

deeply infect the dose-setting, especially multiple-dosing, during the therapy of multiple

sclerosis, AIDS or cancer. Because of THCs lipophilicity the drug accumulates in body

fat and rapidly penetrates in well-perfused organs like liver or heart. There is some

evidence that cannabinoids are hepatotoxic and can lead to liver diseases with regular

consumption and it also has an effect on the heart rate and may lead to a tachycardia.

Repeated cannabis consumption cannot only lead to serious illnesses, but also affects

the brain(Solimini et al., 2017). Thus repeated cannabis intake leads to dependence

and during abstinence to withdrawal symptoms it is important to understand the

metabolization and drug distribution in order to be able to control or antagonize these

effects. The Diagnostic and Statistical Manual of Mental Disorders (DSM-V) recognizes

these symptoms as cannabis use disorders (CUD’s). Hence it is very important to

understand the interactions between concentrations of THC and cannabinoids

receptors, to effectively optimize the dose and address some of the undesired effects

associated with this compound.

27

Table 1-1. Clinical Studies to characterize the PK-PD of THC using various routes of Administration (1980-2014)

Data Set /Author

Route THC (Dose in mg or %)

Dosing Interval (h)

No of Subjects

Sampling Time (h)

Age + bodyweight

1 Ohlsson (1980)

i.v. oral

smoking

5 20 19

0.03 1 cookie

1 cigarette

11 (M) 0-4 0-6 0-4

18-35y

2 Perez (1982)

smoking 1.32 | 1.97 | 3.54

As long as they reach their usual

level of high

6 (3F,3M) 0-6 26-32y

3 Toennes (2008)

smoking 33 / 500µg/kg

BW

0.17 12 heavy users

(9M,3F) + 12

occasional (8M,4F)

0-8 20-31y |20-27y

4 Wall

(1983)

i.v. smoking

2.2|4.0 15|20

0.25-0.42 12 (6M,6F) 0-72 23y, 54-72kg 23y,56.6-

66.2kg 5

Ahmed (2014)

oral 3 |5 |6.5 Single dose, every 2 weeks

12 (6M,6F), data of 11 included

0-2 ≥65y,

6 Schwilke (2009)

oral 20 7 days (every 4-8h, escalating total daily

doses up to 40-120mg)

6 (M) 0.2-4 18-45y

7 Sachse (2009)

oral 15 Single dose 43 (16F, 27M)

0-24 18-65y, 18-25 kg/m2

9 A. Oh (2017)

oral 4.25liq | 5.0capsule

Single dose 54 (M,F) 0-48 18-50y, ≥ 60kg

8 Naef

(2004)

i.v. smoking

0.053 mg/kg 0.03 Complete inhalation solution

8 (4F,4M) 0-8 F: 26-35y; 60 M: 27-50y;

80

10 Goskonda

(2012)

oral 10liq|10 capsule Single dose 18 - -

11 Zurmann (2008)

smoking 2 468 Every 1.5h 12(M) 0-1.33 21-27y 83kg

12 Heuberger

(2014)

i.v. oral

smoking

4.55 5 |6.5 | 8

24 6

Single dose Single dose

1.5

9 13 40

0-48 0-11

0-0.95

18-35y N.A. 22.4y, 22

kg/m2

21.4y, 22.6 kg/m2

28

CHAPTER 2 LC-MS/MS METHOD DEVELOPMENT AND VALIDATION

Background

Marijuana refers to the dried flowers and small leaves of the female hemp

(Cannabis sativa plant) and is among the oldest crops in the world. The plant is known

to contain more than 400 compounds of which at least 60 belong to the cannabinoids of

which Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are most commonly

researched compounds(Huestis, 2007). THC is one of the most commonly abused illicit

drug across the globe and smoking is the most prominent method of

administration(Panlilio et al., 2015). Though it is considered a schedule 1 substance in

the United states, THC (Marinol®) is prescribed to stimulate appetite to treat AIDS

related anorexia’s In Europe, the drug Sativex® is on the market which contains a

combination of ∆9-tetrahydrocannabinol (THC) and Cannabidiol (CBD) as a spray

available for the therapy of multiple sclerosis(Karschner et al., 2011; Pharmaceuticals,

2006). Both THC and CBD bind to the Cannabinoid-1 (CB1) receptor, located primarily

in the central nervous system to elicit most of the pharmacological actions(Lupica et al.,

2004). SR141716A (rimonabant) which is highly selective for CB1 has antagonist and

inverse agonistic properties. It was developed by Sanofi Aventis to treat obesity and

was later withdrawn from the market for safety reasons(egan, Amy G; Colman, 2007;

Moreira and Crippa, 2009). Rimonabant has also shown to antagonize symptoms in the

cannabinod tetrad in mice(Wilson et al., 2006) and is one of the most commonly

administrated compound to study and compare effects of THC and other synthetic

cannabinoids with THC in various animal studies(Järbe et al., 2011; Katsidoni et al.,

2013; Niyuhire et al., 2007; Wilson et al., 2006). Rimonabant is typically administrated

29

intraperitoneal (i.p) to counter act the effects of THC and other synthetic cannabinoid

receptor 1 agonists and understanding the pharmacokincetics of rimoanabant would

help better understand and the dose at which the cannabinoid effects are antagonized.

Various bioanalytical methods have been reported to quantify cannabinoids in both rat

and human plasma. However, most methods developed for rats had 5-10 ng/ml as their

LLOQ(Valiveti and Stinchcomb, 2004; Zgair et al., 2015). THC is a compound which is

known to have long half-life due to its lipophilic nature, hence the redistribution from the

fat tissue becomes the rate limiting step which contributes to prolonged half-life. Having

a lower LLOQ will help characterize the distribution and elimination. Other methods

developed in rats were developed in blood and or more than 100 ul of the matrix which

would be challenging when performing a pharmacokinetic study(Deiana et al., 2012;

Manwell et al., 2014; Palazzoli et al., 2018; Zgair et al., 2015). Decreasing the volume

typically causes low sensitivity for effective quantification of the compound. The main

aim of this study was to develop a sensitive and specific analytical method using LC-

MS/MS with a short run time to determine the plasma concentrations of CB1 receptor

agonists THC,CBD and the antagonist Rimonabant simultaneously in rat plasma. The

primary reason for the development and validation of this new method despite

previously published methods is to apply it to develop an animal model for passive

cannabis smoke inhalation in rats, where we can expect low concentrations of

cannabinoids due to low and variable bioavailability. Most of the previously published

studies were able to quantify cannabinoids after systemic administration, where the

plasma concentrations are higher. In our study we aim to characterize the

pharmacokinetics of the molecules after rats were exposed to cigarette smoke

30

generated using cannabis cigarettes containing 5.3% THC (< 0.001% CBD) and freshly

prepared rimoanabant was injected (i.p) immediately upon removal from the smoke

exposure chamber.

Materials and Methods

Chemical and Reagents

Tetrahydrocannabinol, Cannabidiol, deuterium labeled Delta-9-THC (THC-D3) for

mass spectroscopy (~99% pure) were purchased from Ceriliant (Texas, USA). Double

distilled water was obtained in-house (Department of Pharmaceutics, University of

Florida). All other reagents and chemicals such as methanol, acetonitrile, ammonium

formate, and formic acid were purchased from Fisher Scientific (PA, USA).

Instrumentation and Chromatographic Conditions

Shimadzu UFLC-Nexera X2 system (Kyoto, Japan) consisted of an LC-30AD

pump, SIL-30AC autosampler, CTO-30A column oven and DGU-20ASR degasser. The

ionization and detection of the analytes were carried out on the ABSCIEX API 5500 Q-

Trap mass spectrometer (AB Sciex, Framingham, MA, USA). All data analysis was

performed with the Analyst 1.6 software (AB Sciex, Framingham, MA, USA).The mobile

phase A was 10mM Ammonium formate buffer with 0.1% formic acid and mobile phase

B consisted of methanol. Separations were achieved using isocratic elution at a flow

rate of 1 mL/min. The sample injection volume was 20 µL and the total analysis run time

of a single injection was six minutes.

Bioanalysis of the analytes was performed using an ABSCIEX API 5500 Q-Trap

mass spectrometer (AB Sciex, Framingham, MA, USA) coupled with a Turbo V Ion

Source operated in the positive electrospray ionization (ESI) mode. The mass

spectrometric detection of the analytes and the IS was performed in the multiple

31

reaction monitoring modes (MRM). Tuning by infusion the analytes (10 µL/min) and IS

was performed to determine various compound dependent and source dependent

parameters.

Stock and Standard Quality Controls

Stock solutions (1 mg/ml) of THC, CBD, THC-D3 and rimonabant were prepared

in methanol and DMSO respectively. A mixed stock containing THC, CBD and

Rimonabant were prepared in methanol at a concentration of 10 µg mL-1 From that

mixed stock working standards were freshly prepared and spiked (5 µl) into blank

plasma (45 µL) to obtain concentrations of 0.1-100 ng/mL. Five different quality control

(QC) samples viz, lower limit of quantification (LLOQ) low limit of quantification (LQC),

middle quality control 1 (MQC1), middle quality control 2 (MQC2) and high-quality

control (HQC) were prepared daily in 5 replicates. All the stocks were stored at the

freezer at 2-8 °C and found to be stable during the whole validation.

Extraction Procedure

The extraction of cannabinoids and rimonabant from rat plasma was done using

a 2 step process involving protein precipitation (PPT) followed by liquid-liquid extraction

(LLE). THC-D3 was diluted in acetonitrile to obtain a concentration of 100 ng/mL, and

100 µL of the solution was added in order to precipitate the proteins. The samples were

vortexed for 10 seconds, and 1000 µL of hexane was added to this. After vortexing for

another 30 seconds, the samples were centrifuged at 10000 rpm for 10 minutes. To

finish the extraction 900 µL of the supernatant was transferred into a new vial and dried

using nitrogen evaporator for 10 min. Dried samples were then reconstituted with 100

µL of methanol, and 20 µL was injected into the chromatographic system.

32

Assay Validation Procedure

The LC-MS/MS method was validated as per USFDA Bioanalytical Method

Validation guideline, covering validation parameters like linearity, specificity, and

selectivity, accuracy and precision, matrix effects, stability and extraction and recovery.

The calibration curve consists of a blank sample (blank plasma without analyte or IS), a

zero sample (plasma + IS) and 8 non-zero samples (0.1-100ng/ml). By using the peak

area ration of the analyses and the IS against the standard nominal concentration of

calibration standards in the matrix the calibration curve was plotted.

Selectivity and sensitivity

The selectivity was investigated by analyzing processed blank plasma collected

from six different batches with samples spiked with analytes at LLOQ. Specificity was

established as were no interfering peaks at the retention time of the analytes and IS.

Sensitivity was established as the lowest analyst concentration (LLOQ) which was

within the acceptable accuracy and precision limits.

Stability and recovery

Recovery was conducted at 3 QC levels (LQC, M2QC and HQC) in triplicates.

Where the peaks areas of the extracted samples were compared with non-extracted

acetonitrile and water samples to obtain relative recovery and absolute recovery. The

stability of all the analytes were done at LQC, M2QC and HQC in duplicates. Three

cycles of freeze thaw, bench top, short term and long term were performed. Samples

were considered stable if the accuracy and precision were within the acceptable limits.

33

Intra-day and inter-day accuracy and precision

The intra-, and inter-day accuracy and precision were determined by analyzing 5

replicates of QCs (LLOQ, LOQ, M1QC, M2QC and HQC) in rat plasma. The results

should be within 15% deviation except for the LLOQC, which should be within 20%

deviation from the nominal concentration value.

Matrix effect

Matrix effect on ion suppression or enhancement or extraction efficiency was

addressed by comparing peak areas of the QCs (LLOQ, LQC, M1QC, M2QC and HQC)

spiked in processed blank samples to QCs spiked in neat solutions performed in six

replicates.

Results

Method Development

Various LC conditions were optimized during the method development to get

peaks with best sensitivity and symmetrical. Mobile phases which were used to achieve

chromatographic separation consisted of methanol and 10 mM ammonium formate

buffer containing 0.1% formic acid. Robustness of the method was evaluated against

various columns from 50 µm to 150 µm in length. The best separation with good

sensitivity and peak shapes was achieved with Waters Symmetry, C18 column (150 mm

× 4.6 mm i.d. 5 µm) column. ESI positive mode was finalized for ionization after

comparison between electrospray ionization (ESI) and atmospheric pressure chemical

ionization source (APCI). Higher sensitivity was observed with ESI, relative to APCI.

The most abundant parent/daughter ions, compound dependent and source dependent

parameters were given in the Table2-1. A stable isotope labelled analyte or structural

34

analogue is desirable as IS in mass spectrometry, hence THC-D3 was chosen as the

internal standard.

Method Validation

Selectivity

The method showed good selectivity as no endogenous peaks interfered with the

peaks of analytes. The retention times of CBD , THC and Rimonabant were 2.6 , 4.8

and xx min respectively. The peaks of the IS and analytes showed little variability with

the relative standard deviation (R.S.D) within the acceptable +- 15%.

Calibration curve and linearity

A calibration curve cure for rat plasma ranging from 0.1 to 100 ng/ml was

constructed using 8 calibrators. The average intra-day and inter-day regression

coefficient was found to be >0.99. With this method the LLOQ with %R.S.D <20 and a

signal to noise ratio (S/N) > 10 was seen at 0.1 ng/ml.

Extraction efficiency and matrix effect

The % mean recovery of the analytes are shown in Table 3. This data suggest

that PPT followed by LLE provides efficient and reproducible recovery for all the three

analytes. No significant matrix effect was observed in all of the six-tested rat plasma lots

at the LLOQC, LQC, M1QC, M2QC and HQC concentration.

Accuracy, precision and stability

Accuracy and precision were calculated at 5 different QC levels LLOQ, M1QC,

M2QC and HQC (n=5) for 5 days. The intraday and interday variability was less than

15% at all the concentrations indicating the method was accurate and precise or the

given concentration range from 0.1-100 ng/ml.

35

Summary

A rapid, sensitive, reproducible and robust bioanalytical method was developed

for THC, CBD, and rimonabant in the rat plasma using LC-MS/MS. The present method

enables the quantification of analytes up to 0.1 ng/mL precisely which helps in

understanding the elimination phase in a better way. Moreover, this method requires

only 50 µL for the processing, and this low volume of sample requirement is helpful in

the rodent study. This method can be used in the routine analysis of THC, CBD or

rimonabant in the complex matrices such as plasma. To the best of our knowledge, this

is the first developed and validated sensitive method which enables the quantification of

cannabinoid-1 receptor agonist’s and antagonist in a single run with shown application

in an inhalation pharmacokinetic study.

36

Table 2-1. Optimized source and compound dependent parameters for THC, CBD, Rimonabant and THC-D3

Source Parameters Value Curtain Gas (psi) 10 Collision Gas (psi) Medium IonSpray Voltage (eV) 5500 Source Temperature (°C) 500 Ion Source Gas 1 (psi) 45

Ion Source Gas 2 (psi) 35

Compound Parameters THC CBD Rimonabant THC-D3

Q1 Mass (Da) 315.3 167.2 463.2 318.2 Q3 Mass (Da) 193.1 177.4 363.2 196.2 DP (ev) 74 74 90 74 CE (ev) 33 33 62 33 EP (ev) 13 13 13 13 CXP (ev) 9 9 9 9 Dwell Time (msec) 200 200 200 200

37

Table 2-2. Accuracy and Precision for within batch and between QC samples

Level Nominal Conc.

(ng/mL)

Intra-day Inter-day

CV (%) % Bias CV (%) % Bias

THC

LLOQ 0.1 3.76 8.00 4.40 7.11

LQC 0.3 4.48 -7.08 7.84 3.53

M1QC 5 4.34 4.52 6.57 -0.16

M2QC 40 6.17 6.93 6.45 3.93

HQC 80 7.00 3.27 6.37 2.93

CBD

LLOQ 0.1 5.16 13.60 6.58 10.33

LQC 0.3 6.54 2.80 7.10 7.24

M1QC 5 5.88 -1.24 8.29 -2.61

M2QC 40 8.35 5.30 10.24 1.74

HQC 80 7.73 10.55 9.68 0.62

Rimonabant

LLOQ 0.1 8.10 11.53 6.26 9.28

LQC 0.3 7.48 -7.33 7.68 -3.87

M1QC 5 5.86 -4.01 7.61 -1.40

M2QC 40 6.52 8.55 8.98 1.13

HQC 80 5.50 10.61 5.81 8.96

38

Table 2-3. Mean extraction recovery of analytes in rat plasma

Analytes LLOQ (%) LQC (%)

M1QC (%)

M2QC (%)

HQC (%)

THC 74.32 ± 6.50 79.23 ± 5.75 77.83 ± 4.26 75.84 ± 4.21 75.03 ± 3.89

CBD 77.85 ± 5.55 72.73 ± 5.92 74.79 ± 5.79 72.85 ± 7.77 76.02 ± 5.62

Rimonabant 83.75 ± 6.64 82.53 ± 7.24 85.16 ± 7.51 81.90 ± 5.55 82.08 ± 3.57

Table 2-4. Mean Stability of analytes in rat plasma

Level

Nominal Concentration (ng/mL)

Long-term Stability (At -70 ± 10 °C, 30 days) % Accuracy

Short-term Stability (At 20 °C, 24 hr) % Accuracy

Freeze-Thaw Stability (At, -70 ± 10 °C % Accuracy

Autosampler stability (At 20 °C, 24 hr) % Accuracy

THC LQC 0.3 108.30 103.20 112.80 105.30 HQC 80 97.20 104.40 109.20 99.97

CBD LQC 0.3 106.50 97.50 108.50 103.60 HQC 80 103.30 99.60 105.21 96.80

Rimonabant LQC 0.3 96.90 95.70 106.25 104.50 HQC 80 99.40 104.30 109.50 102.30

39

Figure 2-1. Figure Structures of THC, CBD and Rimonabant Chemical structures of (a)

∆9-tetrahydrocannabinol (b) cannabidiol and (c) rimonabant.

40

Figure 2-2. Representative Chromatograms for the compounds Representative MRM ion-chromatograms of (a) blank rat plasma (THC, CBD and IS), (b) blank rat plasma (Rimonabant & IS), (c) Calibration curve sample chromatogram THC (4.05 min) & CBD, (2.24 min) min), (d) Calibration curve sample chromatogram Rimonabant (2.63 min), (e) chromatogram of PK sample of THC, CBD and IS and (f) chromatogram of PK sample of Rimonabant & IS

41

CHAPTER 3 PHARMACOKINETICS OF TETRAHYDROCANNABINOL IN RATS

Background

THC is a lipophilic compound which readily gets distributed into excessively

perfused organs like the brain and spinal cord, followed by adipose tissue. It is mainly

metabolized by in the liver by cytochrome P450 2C9 and 3A4 enzymes. However, the

rate limiting steps in the metabolisms is diffusion of THC back from the tissues such as

fat, which contributes to its long half-life. The major metabolite is 11-OH

tetrahydrocannabinol which is an active metabolite. Smoking is the most preferred way

of abusing in humans due to dose titration. THC is most commonly administered

systemically in animal models and a the terminal half -life ranges from 1.3-7.3 (h) in

rats(Valiveti et al., 2007; Zgair et al., 2016, 2015). A summary of the pharmacokinetic

parameters observed in rats has been presented in Table 3-1. The dose-effect

relationships have been characterized after parenteral administration, but smoking

remains the most preferred way, and only a few studies have investigated the effects

after exposing rodents to crude marijuana smoke(Nguyen et al., 2016; Wilson et al.,

2006).The purpose of this study is that, since smoking is the most preferred route of

administration in humans, establishing an animal model of smoke exposure to predict

the pharmacokinetics of THC, would give a better insight into the underlying

mechanisms. This animal model can then be applied to study various behavioral effects

of THC.

42

Materials and Methods

Subjects

Male Wistar rats (Charles River, Raleigh, NC) with jugular vein characterization

weighing 300-350 g were used. The rats were shipped to the UF Animal Care Services

(ACS) center a week before the study started in order for acclimatization. Animals were

socially housed (2 per cage) in a temperature and humidity controlled vivarium and

maintained on a 12 hour light-dark cycle. All experiments were performed during the

light phase of the cycle. Food and water were available ad libitum in the home cages.

All subjects were treated in accordance with the National Institutes of Health guidelines

regarding the principles of animal care. Animal facilities and experimental protocols

were in accordance with the Association for the Assessment and Accreditation of

Laboratory Animal Care (AAALAC) and approved by the University of Florida

Institutional Animal Care and Use Committee.

Drugs

Cannabis cigarettes containing 5.3% THC and pure THC dissolve in methanol

were provided by NIDA. Rimonabant was purchased from Sigma Aldrich. THC was

dissolved in mixture of ethanol, PEG, HPBC and the volume was made up with saline to

attain the desired concentration. Rimonabant was dissolve in a mixture of DMSO and

TWEEN 80.

Drug Analysis

THC, Rmionabant plasma concentration were determined using the method

which was developed and validated in chapter 2. In brief, calibration curve sample and

quality control samples were prepared on the day of plasma analysis. 50 ul of plasma

which was collect from the subject was then spiked with 100 ul of ACN containing (IS).

43

To this mixture 1000 ul of hexane was added and 900 ul was separated out after

centrifugation of 10 min. The supernatant was dried and reconstituted in 100 ul of

methanol. 20 ul of the sample was injected in the LCMSMS system using 10 mm

Ammonium formate buffer containing 0.1% formic acid and methanol as the aqueous

and organic mobile phases.

Experimental Design

The study was performed with the approval of the Institutional Animal Care and

Use Committee (IACUC) of the University of Florida (Protocol #). All experiments were

conducted between 8:30 and 10:30 AM. Animals were randomly assigned to the three

treatments (1 mg/kg (i.v), 5 cigarettes, 10 cigarette, 5 cigarettes + rimonabant) n = 3-4

per group). For the first group THC was administered through intravenous (i.v) injection.

The second and third groups were exposed to 2 different durations of cannabis smoke

i.e 50 and 100 min respectively and for the fourth group rimonabant was administered

(i.p) immediately upon completion of smoke exposure. Each blood sample withdrawn

was replaced by an equal volume of heparinized 0.9% saline (20 IU heparin/mL). Serial

blood samples were collected in heparinized microtubes at 10, 20, 40, 60, 120, 240, 480

and 600 minutes after drug administration. Only for THC (iv) 1 mg/kg an additional 24 hr

time point was collected. Plasma (200 µL) was separated from the blood by

centrifugation at 10,000 rpm for 5 minutes and frozen at 80 °C until analysis.

Dose Metrics

Dose calculations for inhalation studies are usually not straight forward. Some of

the sources of variability include particle size, location of the deposited fraction and

mode of exposure. Typically a theoretical estimate can be obtained using various

approaches. Previous literature presented an indirect way of calculating the delta-9-thc

44

doses for marijuana inhalation studies. Only a fraction of the total amount of THC is

present is available for inhalation as a part of it is lost due to pyrolysis and also as side

stream smoke. Data suggested that about 50% of the THC is lost due to pyrolysis and

only 30-45% of the remaining drug is available for inhalation after accounting for loss of

drug due to side stream smoke(Rosenkrantz and Braude, 1974) and only about 80% of

the lung deposited dose will reach the systemic circulation. Hence all these factors must

be accounted for while calculating the delivered dose. The Teague enterprises smoke

machine characterized the content of the cigarettes smoke and provided the calibration

curves of nicotine cigarettes to under the amount of nicotine smoke exposure. The

concentration inside the exposure chamber was 12.5 ug/L when 125 was the TSP

(mg/m3) after 5 cigarettes, we calculated the ratio after each nicotine cigarette and this

showed a proportional increase with an r2 of 0.99 in the used the ratio (mean) of TSP

to Nicotine (0.09) to calculate the dose of TSP to delivered THC for concentrations

inside the chamber. The TSP values after 5 THC cigarettes was 134.6 mg/m3 which

would deliver a concentration of 13 ug/L. Based on these assumptions we calculated

the concentration of the drug which would be present in the exposure chamber (264L)

for the animals to inhale. After obtaining the concentration of THC inside the exposure

chamber (using both the indirect method and using tsp values) we calculated the lung

deposited dose using the following equation(Alexander et al., 2008):

𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝐷𝑜𝑠𝑒 (𝑚𝑔

𝑘𝑔) = 𝐶 ∗ 𝑇 ∗ 𝑅𝑀𝑉 ∗

𝐷𝐹

𝐵𝑊

𝑅𝑀𝑉 = 0.608 ∗ 𝐵𝑊(0.852)

(3-1)

(3-2)

45

Where C is Concentration of the drug (mg/L), T is Total time of exposure (min),

RMV is Respiratory mean volume, DF is the Fraction deposited, BW is Body weight

(kg).

The delivered dose was calculated to be 0.05 mg/kg and 0.2 mg/kg (using both

the methods) where 10% of the total drug was assumed to be the deposition factor.

Typically for rodent 10% is used and 25% is assumed for non-rodent species. Plots and

Tables used for dosimetry provided at the end of chapter Figure 3-4, Table3-7,

Table 3-8.

Data Analysis

Noncompartmental analysis (NCA) of the data was conducted with Phoenix

version 8 (Pharsight Corporation, Mountain View, CA). To determine the basic structural

PK for THC and Rimonabant, two and three compartment models were tested for THC

and one and two compartment models were tested for Rimonabant (Figure 3-1, 3-2, 3-

3). The purpose of the these structural models was to be able to simulate the

pharmacokinetics profiles for the given study design which involved repeated dosing

and relate this to the observed pharmacodynamics. Then population PK parameters

were estimated using nonlinear mixed-effects modeling as implemented in the

NONMEM software version 7.The population PK parameters of were estimated using

both was used for data processing and graphically presenting. The three compartment

model was parameterized in terms of the clearance (CL), Central Volume (V1),

Peripheral Volumes (V2 and V3) and clearance governing the distribution from V1 to V2

and V1 to V3 by Q1 and Q2 respectively. The Advan 6 and TOL=3 subroutines were

used in NONMEM for THC. Data obtained after smoke exposure studies was modeled

46

as an intravenous infusion where the total amount of calculated dose was delivered at

rate such that it was administered in 50 and 100 min respectively. The Two

compartment model for rimonabant was modeled using ADVAN 4 TRANS 4 subroutine,

which was parameterized for rate of absorption (ka), V1 and V2 for the central and

peripheral volume, CL and Q representing the total and inter compartmental clearance.

Assuming that the variability between subjects (ISV) follows log normal distribution,

exponential error model was used to account for ISV variability:

𝑇𝐻𝐸𝑇𝐴𝐼 = 𝑇𝐻𝐸𝑇𝐴𝑇𝑉 ∗ 𝐸𝑋𝑃(𝑛𝑖) (3-3)

Where, THETAi is Individual parameter ,THETATV= Typical values or population

means for the respective parameters, ήi= ISV value (normally distributed).

A proportional error model for THC and a combined error model for rimonabant

was used to explain residual error. Selection was done based on inspection of GOF’s.

The residual error model explains all the other error which the IIV does not

capture for example structural model misspecification or an analytical measurement

error.

Model selection and identification was based on the likelihood ratio test, PK

parameter point estimates and their respective confidence intervals, parameter

correlations, and GOF’s.

A decrease of 6.6 or 9.2 points in the OFV after addition of one or two

parameters respectively is considered significant difference of OFV assuming that the

difference between the OFV follows a chi squared distribution.

𝐴𝐼𝐶 = 𝑂𝐹𝑉 + 2 ∗ 𝑃 (3-4)

47

where p = total number of parameters in the model (structural + error). For

hierarchy model comparison, the model with lower value of AIC was considered better.

Over all model fitting was asses by plotting the observed plasma concentration of all

individuals and population prediction (PRED).GOF”s were also subjected to visual

inspection to detect systemic deviations from the model fits residuals (RES) versus

PRED, individual observed concentration (DV) versus PRED or individual predicted

(IPRED) values, and conditioned weighted residuals (CWRES) versus PRED. Finally

visual predictive check (VPC) were done for internal validation.

Results

Non-Compartment Analysis of THC

The pharmacokinetic profile of THC after intravenous and passive smoke

exposure is shown in Figure 3-5, 3-6. Upon obtaining the pharmacokinetic profile a non-

compartmental analysis was performed in order to estimate the pharmacokinetic

parameters for both the routes of administration. The maximum concentration of THC

(Cmax) was seen as soon as the rats were taken out from the exposure chamber and

the concentrations declines with a terminal half life of 3.7 hours. Total AUC0-t (h*ug/L)

of THC after 0.05, 0.2, 1 mg/kg was 12.2, 38.2 and 276. The calculated r2 for AUC was

0.99. The clearance was calculated to 1.2 (l/h) and the volume of distribution was 4 L.

Results from the NCA are presented in Table 3-1.

Non-Compartment Analysis of THC when Co-administered with Rimonabant

The pharmacokinetic profile of both the compounds are shown in Figure 3-8 and

Figure 3-9. Upon obtaining the pharmacokinetic profile a non-compartmental analysis

was performed in order to estimate the pharmacokinetic parameters for both the

compounds. THC declines with a terminal half-life of 3.7 hours, total AUC0-t(h*ug/L) of

48

THC after 0.05 mg/kg was 12.2. The clearance was calculated to 1.1 (L/h). The

maximum concentration (Cmax) of 88.6 +/-5.64 for rimoanbant was seen 10 minutes after

i.p administration and it declines with a terminal half life of 5.4 hours total AUC0-t(h*ug/L)

of Rimonabant after 3 mg/kg was 308.15 . The clearance (CL/F) was calculated to 2

(l/h). NCA results for THC with rimonabant and for Rimoanabant are present in the

Tables 3-2 and 3-3 respectively.

Compartment Modelling for THC

The PK compartmental analysis for THC was done using both two and three

compartmental models. The OFV with the two compartment model was 153.97 and with

the three compartment model was 113.88. This was highly significant change, also the

goodness of fit plots fit plots with the 3 compartment model were better compared to the

two compartment model which suggest that the structural model of THC follow 3

compartment distribution. A three compartment model with no between subject yielded

the best fit. An AIC value of 127.7 on the base model dropped down to 115.35 on

addition of 1 parameter (BSV on clearance), suggesting that the mode fitting showed

significant improvement, but the base model was retained as the final model due to

easy of application. The parameters for clearance (L/h) and compartmental volumes (L)

were 0.85 and .56, 0.49, 1.26 respectively. The parameters for THC population model,

goodness of fit plots ,VPC’s and individual fits for are presented in Table 3-5 and Figure

3-10, 3-11, 3-12 .

Compartment Modeling for Rimonabant

The PK compartmental analysis for rimonabant was done using both one and

two compartment models. The OFV with the one compartment model with absorption

(ka) was 178.19 and with the three compartment model was 157.79. This was highly

49

significant change, also the goodness of fit plots fit plots and the individual plots with the

2 compartment model were better compared to the one compartment model which

suggest that the structural model of rimonabant follows 2 compartment distribution. The

two compartment model with between subject variability on clearance from the central

compartment (CL/F) yielded the best fit. The AIC value (167.7) on the base model

dropped down to 157 on addition of 1 parameter, suggesting that the model was not

over parameterized. The parameters for apparent clearance (L/h) and compartmental

volumes (L) were 1.97 and 2.13, 13.36 respectively. The rate of absorption from the

dosing compartment to central compartment (ka) was estimated to be 2.8h-1. The R.S.E

of the predicted values was within the acceptable limits. The IIV for CL/F was 0.04. The

parameters for rimonabant population model, goodness of fit plots, visual predictive

checks and individual fits are presented in Table 3-6 and Figure 3-13, 3-14, 3-15.

Discussion

In this study the pharmacokinetics of Tetrahydrocannabinol in male wistar rats

with doses 0.05, 0.2, 1 mg/kg were investigated. And the pharmacokinetics of THC with

co-administration of rimonabant was also investigated. A population three compartment

linear model was proposed to describe the data for THC and a two compartment model

with first order absorption was used to describe the kinetics of rimonabant following

intraperitoneal injections. Validation suggests that the model is a robust model to predict

the population pharmacokinetics of the compounds in rats. Terminal elimination half-life

for THC that were obtained using NCA was consistent with the literature reported values

which ranges from 1.2-8 hours. The parameter estimates for clearance is also in the

range 0.3-4 (L/h). Previously a two compartment model was used to model the

pharmacokinetics of THC after intravenous administration in rats(Valiveti et al., 2007).

50

However, THC is a highly lipophilic drug and gets rapidly distributed in the highly

perfused organs such as the lung and brain, followed by the distribution into the adipose

tissue where it gets stored for a long period of time. Hence as three compartment model

would better explain the distribution of THC compared to a two compartment model.

The population pharmacokinetic in humans has also been described a 3 compartment

population model(Heuberger et al., 2015). The estimates for clearance which were

obtained is close the hepatic blood which is around 1.5 (L/h) for rats which indicates a

high extraction drug as reported in the literature. Therefore based on these results we

can say that the final three compartment model assuming linear PK is reasonably

powerful in order to predict the plasma concentration of tetrahydrocannabinol in rats.

Though numerous studies done in rodent models use rimonabant as a standard to

understand the CB1 receptor mediated effects, much information is not available on the

exposure to link it with the behavior observed in order to get reliable pharmacodynamic

parameters. This further implies that a whole body cannabis smoke exposure model can

be used to explain the exposure-response relationships and help to understand some of

the underlying mechanisms of cannabinoid dependence and withdrawal.

51

Table 3-1. Phamramcokinetic Parameters of THC in rats reported in literature

PK PARAMETER

THC IV THC IV THC IV THC Intranasal (PEG)

THC Intranasal (PEG+ Ethanol)

Dose (mg/kg) 1 4 5 10 10 Half-life 7.27±01.95 4.6±2 1.42 1.1±0.13 1.7±0.59 CL (l/h)/CL/F* 3.37±1.47 0.9±0.2 0.31 5.07±1.9* 3.60 ±0.59* VolumeSS (L) 9.69±5.5 2.37±0.1 0.29 - -

AUC0-t(h*ug/L) 331±119 1624±334 4679 212±79 301±100

Table 3-2. Phamramcokinetic Parameters obtained after performing NCA using

Phoenix8.0

PK PARAMETER THC IV THC Inhalation THC Inhalation

Dose (mg/kg) 1 0.05 0.2 Half-life 8.8±0.5 3±0.8 3.3±0.7 CL (l/h) 1.05±0.01 1.1±0.15 1.4±0.15 VolumeSS (L) 3.56±0.06 3.3±1.02

4.1±0.11

AUC0-t(h*ug/L) 276.06±4.5 12.4±1.75 38.2±3.8 AUCinf(h*ug/L) 285.05±3.3 13.1±1.6

41.4±4.7

Table 3-3. Pharmacokinetic Parameters obtained after performing NCA using when

Rimonabant was co-administered with THC

PK PARAMETER Rimonabant

Dose (mg/kg) 3 Half-life 5.4±0.85 CL (l/h) Volume (L) AUC0-t

2±0.3 15.5±1.06 308.15±55

Table 3-4. Phamramcokinetic Parameters of THC upon co-administration with

Rimonabant obtained after performing NCA using Phoenix8.0

PK PARAMETER THC Inhalation

Dose (mg/kg) 0.05 Half-life 3.7±1.3 CL (l/h) Volume (L) AUC0-t

AUC0-inf

1.1±0.26 3.9±2.07 12.2±3 13.9±3.8

52

Table 3-5. Phamramcokinetic Parameters obtained after performing compartmental analysis for THC

PK PARAMETER THC- IV RSE %

CL (L/h) 0.85 5 V1 (L) 0.56 15 V2 (L) Q2(L/H) V3(L) Q3(L)

0.49 0.46 1.26 0.14

16 24 10 12

Table 3-6. Phamramcokinetic Parameters obtained after performing compartmental

analysis for Rimonabant

PK PARAMETER Rimonabant-IP RSE %

CL/F (L/h) 1.97 9 V1/F (L) 2.13 3 V2/F (L) Q (L/H) Ka (h-1) IIV_CL

13.36 16.53 2.80 0.05

4 19 14 45

53

Table 3-7. Nicotine Chamber concentration vs TSP

Cigarette TSP (mg/m3) Nic (ug/L) (Chamber) Ratio(NIC/TSP)

1 25 2.5 0.1

2 40 5 0.12

3 50 7.5 0.15 4 100 10 0.1 5 125 12.5 0.1

6 150 13.5 0.09 7 175 15 0.08

8 200 16.5 0.08 9 250 18 0.07 10 325 20 0.06

Mean 0.09

Table 3-8. THC Delivered dose calculation.

Cig Weight(g) THC Amount of THC (g)

Toal THC (mg)

THC remaining after Pyrolysis (50%)

Available for animal inhalation(30%)

Volume of chamber (L) mg/L

Number of exposure cages

inside the chamber (4)

1 0.9 5.30% 0.047 47.7 23.85 7.155 264 0.027

0.006

5 3 0.14 143.1 71.55 21.465 0.08

0.02

10 7.2 0.34 343.44 171.72 51.516 0.19

0.047

54

Figure 3-1. One Compartment Pharmacokinetic Model

Figure 3-2. Two Compartment Pharmacokinetic Model

55

Figure 3-3. Three compartment pharmacokinetic model

Figure 3-4. TSP (mg/m3) vs Nicotine (ug/L)

56

Figure 3-5. Plasma Concentration Of THC after Intravenous Administration

57

Figure 3-6. Plasma Concentration of THC after passive smoke inhalation (0.05 mg/kg)

58

Figure 3-7. Plasma Concentration of THC after passive smoke inhalation (0.2 mg/kg)

59

Figure 3-8. Plasma Concentration of THC after passive smoke inhalation in presence of

Rimonabant (0.05 mg/kg)

60

Figure 3-9. Plasma Concentration of Rimonabant (3 mg/kg) after smoke exposure

61

Figure 3-10. Goodness of fit plots for THC PK model

Figure 3-11. VPC for THC PK model

62

Figure 3-12. Individual profiles for subject administered THC

63

Figure 3-13. Individual profiles for subject administered rimonabant (i.p)

64

Figure 3-14. GOF’s rimonabant (i.p)

Figure 3-15. VPC rimonabant (i.p)

65

CHAPTER 4 CHARACTERIZATION OF CANNABINOID ABUSE AND WITHDRAWAL

Cannabiniod Abuse

Cannabis plant contains over 100 compounds that are cannabinoids and THC is

the psychoactive ingredient responsible for the psychopharmacological effects. The use

of cannabis and also synthetic cannabinoids such as spice is on a rise on a whole and

especially gaining popularity in young adults. It is estimated that over 8 million people

were using cannabis on a regular basis and more that 800 thousand people were treat

for cannabis use.(Panlilio et al., 2015) Recently FDA has also expressed concerns over

orally consumed cannabis products due to the number of deaths associated with over

dose which can lead psychosis and suicidal symptoms(Benjamin and Fossler, 2016).

Cannabis which is prescribed for medical use which is given orally has also shown to

develop dependence symptoms(Panlilio et al., 2010). Therefore the endocannabinoid

system can be considered as a double edged sword if considered as a potential target

in the process of drug discovery and hence it is important to understand the underlying

mechanism of cannabinoid addiction and abuse potential. Theories of cannabinoid

reward or dependence must be ultimately clinically validated but research in animals

can help understand effects in humans. Although behavioral effects can be conducted

in humans, results are very subject which typically leads to high variability or in

consistent trends. Some of the other advantages of laboratory animals include access

to brain areas which cannot be accessed in a clinical setting to study the underlying

mechanism of addiction due to ethical reasons. Various animal models have been used

to characterize the behavioral effects of cannabinoids. Self -administration studies in

rodents and non-human primates have suggested that THC like other drugs of abuse

66

has rewarding effects(Justinova et al., 2003; Tanda et al., 2000; Zangen, 2006). These

findings further pushed the research to test the effect of cannabinoid administration

under different paradigms such as Intracranial self- administration (IVSA), Intracranial

self- stimulation (ICSS) and Conditioned Place Preference(CPP) to evaluate and

understand different behavior exhibited by the animals under various conditions.,. The

effects were cannabinoid 1 receptor mediated as CB1 blockage antagonized THC’s

actions which resulted in withdrawal in THC dependent mice. (Järbe et al., 2011;

Katsidoni et al., 2013; Lichtman and Martin, 2002; Panagis et al., 2014; Wilson et al.,

2006). Most of the studies which investigated behavioral effects of THC have evaluated

using systemic administration of THC, however smoking in the most common route of

cannabis abuse and relatively few studies were done using inhalation model(Lichtman

et al., 2001b; Nguyen et al., 2016). While it is expected that inhalation of cannabis

smoke can lead to dependence, how other cannabinoids present in the plant material

might interact with THC or extent their own is yet to be fully understood. Studies done

using THC smoke inhalation have suggested that THC can induce dependence by

observing reversal of THC’s effects in the cannabinoid tetrad and somatic signs of

withdrawal when rimonabant was administered to cannabis smoke exposed animal

compared to navie animals(Bruijnzeel et al., 2016; Lichtman et al., 2001b; Wilson et al.,

2006). Studies comparing the effect of second hand smoke exposure in nonsmokers

concluded that the subjective effects were directly related to the amount of THC smoke

exposure and also concluded that passive exposure to high amounts of THC smoke can

mimic active smoking(Cone et al., 2015)Based on the information above, we

67

hypothesized that 2 weeks of cannabis smoke using the whole body exposure rodent

model can be applied in the ICSS setting to quantify the extent of dependence.

Materials and Methods

Subjects

Male Wistar rats (n=36), weight range 225 ± 50 g, were obtained from Charles

River. The animals were kept in an environmentally controlled breeding room for at least

one week acclimatization before the surgery. The animals were housed (2 per cage) in

a temperature and humidity-controlled vivarium and maintained on a 12-h reversed

light-dark cycle (lights off at 8 AM), had free access to food and water at all times.

Animal facilities and experimental protocols were in accordance with the Association for

the Assessment and Accreditation of Laboratory Animal Care (AAALAC) and approved

by the University of Florida Institutional Animal Care and Use Committee.

Drugs

Rimonabant hydrochloride (SR 141716A) was purchased from Tocris Bioscience

(Bristol, United Kingdom). Rimonabant was dissolved in a vehicle of Tween 80 (5%

volume/volume, v/v), DMSO (20% v/v), and sterile saline (75% v/v). Freshly prepared

rimonabant was administered in 3 different doses (0.3, 1, 3 mg/kg). High potency

cannabis cigarettes (5.3 % THC) were kindly provided by NIDA Drug supply program.

The cannabis cigarettes were stored at -20°C. About 24 h before being used, the

cigarettes were taken out and placed in an airtight humidity chamber with a small dish

containing saline and kept at room temperature (22–24°C). The cigarettes were used

within one hour of being removed from the humidity chamber.

68

Cannabis Smoke Exposure

Whole body smoke exposure chamber was used for cannabis smoke exposure.

The rats were transferred into fresh the exposure cages just before the smoke exposure

sessions and returned to their home cages after the exposure sessions, they were not

restrained anytime during the smoke exposure as this is a whole body smoke exposure.

Cannabis smoke was generated using a microprocessor-controlled cigarette smoking-

machine (model TE-10, Teague Enterprises, Davis, CA). Smoke was generated by

burning cannabis cigarettes which would mimic human puff pattern (35 cm3 puff

volume, 1 puff per minute, 2 seconds per puff). Mainstream and side stream smoke was

transported to a conditioning chamber. Smoke from condition chamber was then

transported to exposure chamber after diluting with air. Exposure conditions were

monitored for carbon monoxide (CO) and TSP levels. Carbon monoxide levels were

monitored using (Monoxor III, Bacharach, New Kensington, PA USA) a deceive that can

accurately measure between 0-2000 PPM. TSP was measured by passing the smoke in

the exposure chambers through a pre-weighed filter (Pallflex Emfab Filter, Pall

Corporation, Port Washington, NY USA) for 1-2 min. The total suspended particulate

matter per cubic meter was calculated by dividing the weight increase of the filter by the

volume of the airflow through the filter.

ICSS Apparatus

Twelve Plexiglas chambers (30.5 x 30 x 17 cm; Med Associates, Georgia, VT),

each housed in a sound-attenuating melamine chamber (Med Associates, Georgia, VT).

Stimulation parameters all the data and results was controlled by a computer. Each

chamber had a metal grid floor and a metal wheel (5 cm wide) centered on a sidewall. A

detector was attached the response wheel and recorded every 90 degrees of rotation.

69

Brain stimulation was delivered by constant current stimulators (Model 1200C, Stimtek,

Acton, MA). Rats were connected to the stimulation circuit through bipolar leads

(Plastics One, Roanoke, VA) attached to gold- contact swivel commutators (model

SL2C Plastics One, Roanoke, VA).

Experimental Design

The experiment was done in 3 groups of rats. The total duration of each

experiment from the start to completion is 3 months. Experiment began after 1 week of

acclimatization. Rats are said to be stable only when there is <±10% change in their

mean baseline threshold over a 5 day interval. After obtaining a stable baseline, the rats

are expose to cannabis smoke for the first two weeks and thresholds are recorded daily.

Following two weeks of smoke exposure, the rats were considered dependent based on

our previous research(Bruijnzeel et al., 2016) and freshly prepared Rimonabant (0.3, 1,

3 mg/kg) was administered (i.p) immediately upon completion of smoke exposure using

a latin square design. Figure 4-2 and Figure 4-3 shows the experimental design. Rats

were placed inside the ICSS chamber around 30 minutes after the smoke exposure.

Blood Collection

Blood samples were collected in heparinized microtubes at 60 minutes after

smoke exposure. Plasma was separated from the blood by centrifugation at 10,000 rpm

for 5 minutes and frozen at -80 °C until analysis. The blood samples were collected from

the dorsal pedal vein. This was done by anesthetizing the animal, to locate the dorsal

pedal vein which is present on top of the foot. The foot was sterilized using alcohol and

the dorsal pedal vein was punctured using a 23G needle.. Plasma samples were not

collected during the weeks of Rimonabant administration as it can it can affect the ICSS

thresholds.

70

Drug Analysis

THC plasma concentration were determined using the method which was

developed and validated in chapter 2. In brief, calibration curve samples and quality

control samples were prepared on the day of plasma analysis. 50 ul of plasma which

was collect from the subject was then spiked with 100 ul of ACN containing (IS). To this

mixture 1000 ul of hexane was added and 900 ul was separated out after centrifugation

of 10 min. The supernatant was dried and reconstituted in 100 ul of methanol. 20 ul of

the sample was injected in the LCMSMS system using 10 mm Ammonium formate

buffer containing 0.1% formic acid and methanol as the aqueous and organic mobile

phases.

Statistical Analysis

Statistical Analyses to compare the means prior to starting the threshold was

done using one factor anova with treatment as the between group factor. For, brain

reward thresholds and response latencies are expressed as a percentage of the mean

prior to starting the exposure for result 1 and pre-test day values for result 2. Percent

changes in brain reward thresholds and response latencies were analyzed using two-

way repeated-measures analyses of variance (ANOVA), For experiment one the

between subject effects were treatment and the within subject effect were days and for

the second part with the dose of Rimonabant as the within-subjects factor and cannabis

smoke exposure as the between-subjects factor.

Intracranial Self Stimulation

Rats were trained on a modified discrete-trial ICSS procedure(Kornetsky et al.,

1979) as described previously(Bruijnzeel et al., 2007) Figure 4-1 shows the setup, rats

are trained initially to turn the wheel on a fixed ratio 1 (FR1) schedule of reinforcement.

71

Each quarter turn results in delivery of a 0.5 second train of 0.1 millisecond cathodal

square-wave pulses at a frequency of 100 Hz. Upon obtaining successful acquisition of

responding, the rats were trained on a discrete-trial current-threshold procedure. Each

trial started with the delivery of a non-contingent stimulus, followed by a 7.5 second

response window during which response from the animal resulted in a second

contingent stimulus of the same intensity. A response during this 7.5 second response

window is called a positive response. The inter-trial interval (ITI), which follows either a

positive response or end of response window, has an average duration of 10 seconds

(7.5 - 12.5 seconds). A test session consisted of four alternating series of descending

and ascending current intensities which typically lasted 45 minutes and provided two

variables: brain reward thresholds and response latencies.

Results

Effect of THC Dose and Duration on Baseline Reward Threshold

Mean (± S.E.M.) absolute brain reward thresholds before starting cannabis

smoke exposure were for control and THC treated (0.05, 0.2 mg/kg/day 5 days a week

for 2 Weeks) rats were 129.68 ± 32.03, 103.39 ± 28.12, 125.15 ± 36.57 µA,

respectively. Mean (± S.E.M.) thresholds for control and THC treated (0.05, 0.2

mg/kg/day 5 days a week) rats were 126.57 ± 29.30, 97.66 ± 26.12, 125.46 ± 35.29 µA

after 2 weeks of smoke exposure. The mean latencies were 3.29 ± 0.28, 3.19 ± 0.22,

3.2 ± 0.19 seconds. The mean latencies after two weeks of treatment were 3.35 ± 0.48,

3.22 ± 0.32, 3.41 ± 0.23 seconds. The plasma THC levels both the treatments are

presented in Table 4-1. The plasmas THC concentrations around 45-60 min after

smoke exposure were identical during week 1 and week 2 for both the doses. No

difference due to the dose of THC administered was seen in the plasma levels.

72

Statistical Analyses was performed by doing a two way anova with repeated

measures between the three treatments and the change from baseline each day after

the start of the treatment as within subject factor indicated that there was no statistically

significant effect of THC smoke exposure on the baseline thresholds of subjects. Effect

of THC smoke exposure and the change from baseline are presented in Figure 4-1.

There was on change in the latencies of between the treatments.

Effect of Rimonabant Induced Precipitated Withdrawal on Brain Reward Threshold

Mean (± S.E.M.) thresholds and mean latencies for control and cannabis smoke

exposed (0.05, 0.2 mg/kg/day 5 days a week) rats, after 2 weeks of cannabis smoke

exposure were 126.57 ± 29.30, 97.66 ± 26.12, 125.46 ± 35.29 µA and 3.35 ± 0.48, 3.22

± 0.32, 3.41 ± 0.23 seconds respectively.

Statistical analysis indicated that rimonabant induced an elevation in the ICSS

thresholds in rats exposed to THC smoke compared to control animals. Statistical

analysis was performed using THC treatment was between subject factor and

rimonabant as within subject factor for two way anova with repeated measures. There

were significant elevations in the brain reward thresholds in the cannabis treated groups

compared to the control group which were not exposed to cannabis smoke containing

THC. All the doses of rimoanabnt administered in the THC treated animals was

significantly different compared to their respective control dose (0 mg/kg) (Treatment

1x3mg/kg, Treatment 1x0mg/kg, P<0.001). Treatment 2x3mg/kg, Treatment 2x0mg/kg,

P<0.001, Treatment0 (0.33 mg/kg) X Treatment1,2 (3mg/kg), P<0.001, Treatment0

(0.33 mg/kg) X Treatment2 (1mg/kg), P<0.05). There was no significant difference

between the two doses for changes from baseline reward threshold. Both,

73

0.05mg/kg/day and 0.2mg/kg/day were identical and post hoc analysis did not show

significant difference, however both the groups were significantly different from the

control group. So the rats which were treated with 0.05 mg/kg/day and 0.2 mg/kg/day

behaved similarly after rimonabant administrations. The results for rimonabant induced

precipitated withdrawal is presented in Figure 4-2.

Discussion

The present study investigated the effects of dose and duration of cannabis

smoke exposure on development of cannabinoid dependence. The results in the

present study indicate that chronic exposure to even low doses of cannabis smoke can

to the development of dependence as indicated by negative affective effects with

rimonabant induced precipitated withdrawal.

There was no change from the baseline thresholds of the animals which were

exposed to cannabis smoke vs control, although the trend suggesting some decrease

from baseline on day 5 and 8 of smoke exposure. Results from the two anova with

repeated measure using cannabis smoke exposure as between subject factor and time

as the within subject factor suggested that there no effect on the baseline threshold

values for both the doses and also the duration of smoke exposure. In other words it

suggests that THC did not have any synergistic effects on reducing the amount of

stimulation necessary to sustain responding by lowering the ICSS thresholds. This is in

agreement with the previously reported literature that THC and other synthetic

cannabinoid agonists acting of the CB1 receptors did not have an hedonic or anheodic

affects in the ICSS paradigm in rodents(Grim et al., 2015; Vlachou et al., 2007). The

plasma samples which were collected on (Day 4 and 11) at 60 min after smoke

exposure for both the doses had similar THC concentrations, which can mean THC gets

74

rapidly distributed to the highly perfused organs like the brain and slowly redistributed

back to the blood (rate limiting step) followed by elimination and suggesting that plasma

concentration might not be the best reflection of the true behavioral effects of THC. This

suggests that there might be greater exposure in the brain compared to plasma. This is

finding is consistent with the results which were presented in another study trying to

characterize the amount of dopamine efflux after intraperitoneal administration of THC.

The study results show that there is maximum increase in the dopamine levels at 60

min after the injection which can be correlated to the amount of THC in the brain on the

CB1 receptors in the VTA and nucleus accumbens to increase dopamine efflux(Chen et

al., 1991; Covey et al., 2015).

It has been suggested that repeated exposure to cannabis smoke can lead to

tolerance to the subject effects and also induce physical dependence. The prolonged

half-life of THC can mask the withdrawal experienced by people leading to a conclusion

that no withdrawal is experienced upon cessation. However, precipitated withdrawal is a

technique where an antagonist is administered to displace the agonist from the receptor

to study the withdrawal effects which would be experienced. Hence precipitated

withdrawal is technique which is commonly applied to study cannabinoid withdrawal in

animal models(Lichtman and Martin, 2002; Wilson et al., 2006). In order to determine if

chronic exposure to cannabis smoke lead to dependence we treated the rats with Cb1

receptor antagonist Rimonabant. Rimonabant induced a dose dependent increase in

change in the thresholds with the 3 mg/kg showing the maximum increase from

baseline reward threshold. This finding is consistent with the previous studies which

investigated the effects of rimonabant precipitated withdrawal in animal models

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(Bruijnzeel et al., 2016; Schlosburg et al., 2009; Tsou et al., 1995; Wilson et al., 2006).

There no difference between the elevations in the thresholds for the cannabis smoke

treated groups, but the overall elevations in the cannabis smoke exposed rats were

significantly higher compared to the control rats.

Studies which were performed to understand the effect of Rimoanabant on

dopamine production in the brain reward pathway showed that Rimonabant significantly

decreased the production of dopamine in the nucleus accumbens. These effects were

antagonized by pre-treatment with Cb1 receptor agonists(Lichtman et al., 2001a; Melis

et al., 2007).

It was initially suggested that THC has rewarding like effects in the ICSS

paradigm by Gardner et al (Gardner et al., 1988) after which several cannabinoids were

tested in this paradigm to understand the abuse potential(Grim et al., 2015; Vlachou et

al., 2005). It was also shown that THC has no reward like properties in the ICSS

paradigm(Fokos and Panagis, 2010; Vlachou et al., 2007). It was not until recently that

a biphasic response of THC was suggested wherein THC showed reward-facilitating

effects in the when a low dose was administered and caused aversion when a high

dose was administered(Katsidoni et al., 2013). However one major question which is

still to be address is the effects of THC in the ICSS paradigm after THC smoke

exposure. The findings in our study showed that THC by itself did not change the

baseline thresholds of the animals at both the doses (0.05 mg/kg/day and 0.2

mg/kg/day). This can be related to the amount of DA efflux which might be substantial

enough to see a significant synergy with baseline threshold. This could be due to the

amount of thc entering the brain after administration is not sufficient enough to cause a

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significant increase in the dopamine efflux which could be quantified based on the

change in threshold. There have also been some stain specific efflux in dopamine which

were suggested which could also add to the explanation of the current situation.

Another possible explanation of no change from baseline threshold after cannabis

smoke exposure is the rapid development of tolerance which is associated with

prolonged use of drugs of abuse. This is supported with the finding that there was

reduction in the dopamine efflux for Ex vivo preparations of brain slices containing VTA

neurons for rats which were chronically treated with THC compared to naive

rats(Panlilio et al., 2015).

In conclusion, there results from the present study indicate that chronic exposure

to even low doses of cannabis smoke can lead to the development of dependence. With

no currently approved medication and high relapse rates seen with motivation therapy

there is a need to understand and alleviate withdrawal as patients experience a wide

range of withdrawal symptoms like lack of motivation, appetite, restlessness and

depressed mood. The question about withdrawal associated with chronic cannabinoid

abuse has been answered in this study. Taken together this animals model can be used

to study the effects of cannabis smoke exposure on dependence and also aid to

develop potential candidates to treat cannabinoid withdrawal syndrome.

77

Figure 4-1. ICSS Set Up

78

Figure 4-2. Experimental design

Figure 4-3. Experimental design-2

79

Figure 4-4. Effect of THC Dose and Duration of Exposure on Brain Reward Threshold

80

Figure 4-5. Effect of Rimonabant (i.p) on Brain reward Thresholds

81

Table 4-1. Summary of the total amount of cannabis burnt for both the treatments, the total suspended smoke particulate inside the chamber, duration of smoke exposure and plasma concentration of THC during week 1 and week 2.

Number of Cigarettes

Total Amount of cannabis burnt (g)

TSP (mg/m3)

Carbon Monoxide (PPM)

Duration of Cannabis Smoke Exposure (min)

Plasma concentration of THC day 4(ng/ml)

Plasma concentration of THC day 11 (ng/ml)

5 3.18 145.78 270 50 - 6.51

10 6.4 226.23 393 100 4.43±1.65 6.82

82

CHAPTER 5 PHARMACOKINETIC AND PHARMACODYNAMIC MODELIING OF THC INDUCED

CANNABINOID DEPENDENCE

Pharmacokinetic and Pharmacodynamic (PK/PD) Modeling

Application of kinetics to drugs was called pharmacokinetics (PK) by F. H. Dost in

1953. So it can be called as the change in the amount of drug over time in the body. It

studies the fate of the drug in the body and often describes the concentration-time

course for the drugs or the metabolites in body fluids like plasma and urine(Derendorf,

1998). After a dose of drug is delivered it undergoes absorption, distribution,

metabolism and elimination, and is often called the ADME scheme.

Relation between concentration and effect are defined in pharmacodynamics. It

was first shown by levy who showed correlations between reversible drug effects and

drug concentrations. The interaction between drug and target leads to an effect which is

usually associated with some time delay and this due to the disposition of the drug to

the effect site or some underlying mechanism like transduction pathway leading to the

effect. Parameters like affinity, sensitivity, potency and efficacy are used to characterize

pharmacodynamics. Population pharmacodynamics evaluates effects of the drug

(physiological/biochemical) on disease causing agents at population level(Levy, 1986;

Sharma and Jusko, 1998; Unadkat et al., 1986).

PKPD modelling and simulation emerged as an important tool due to combining

underlying mechanisms with data and knowledge to make rational decisions in the

process of drug development(Upton and Mould, 2014, 2014). Models in a broad sense

are nothing but simple representation of the system and the usually simplified according

to the purpose or the intended use, hence are usually judged by “fitness for purpose”

rather than “right” or “true”. PKPD modelling was first introduced by Sheiner et al

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(Holford and Sheiner, 1982). Modeling and simulation can aid in drug development

from pre-clinical to first in human dosing. It can further aid in selection of doses for

further evaluation and finally extend the selected doses to other populations. PKPD

modelling provides a frame work to understand the drug effects at different dose levels.

It can aid in understanding the variability in PK and PD between different subjects which

is seen with most of the drugs. Typically covariates such as gender, weight, physiology,

co-administration of other drugs, disease state which can lead to variability in the

exposure and response which can be explained by PKPD modelling and simulation.

The data obtained after modelling and simulation can be compared with other drugs

from the same class to evaluate its potential as a new therapeutic agent. Hence,

objective of population modelling is to able to understand the pharmacologic action of

the drug and to be able to develop a mathematical function to explain the

pharmacologic time course of the drug at various dose regimens. PKPD model always

include an observed response (PD) and these can be either continuous or categorical.

Models describing continuous pharmacodynamics typically explain the PKPD relations

using a continuous function like linear, Emax or sigmoidal emax model. The

concentration responsible for the observed effect can be directly linked to plasma

(usually assumed to be central compartment) or concentration at the effect site (other

site in the body) if the observed effect lags behind the exposure of drug in plasma. This

delay in effect can be due to the distribution of the drug to the site of action when it is

not readily accessible (rate limiting step for the effect) and maximal effect cannot be

seen at maximum concentration in the plasma. Also, many drugs induce pharmacologic

response not because of their interaction which the receptors but due to the causing the

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changes in the endogenous compound via some kind of transduction mechanism. In

both the cases the pharmacological response might be seen if when there is no drug

present in plasma.

It was shown that the dose, durations of exposure, dosing frequency, use of

other cross dependent drugs and the physiology of the subject are the PKPK

parameters which explain the underlying mechanisms of dependence and tolerance.

Although physical dependence and tolerance are actions of drug administration which

can be predicted, the predictability of the current models can be called as empirical due

to due to lack of in depth understanding about the mechanisms of actions as a result of

which it is difficult to simplify the underlying complex physiological processes(Gårdmark

et al., 1999).

Methods

The methods and results of physical dependence time courses of THC were

described previously in Chapter 4. To describe the time course of physical dependence

for a given dose regimen, a sequential PK/PD modeling approach was applied after

subtracting the placebo response values at each time point from the mean response

values for the each of the treatments as one group of subjects served as

placebo/control which were exposed to air only. This will give us a better understanding

of the true response after exposure to smoke containing THC 5-1.

PK/PD modeling of THC for animals which were exposed to cannabis smoke containing THC

Although cannabinoids display many central nervous system induced effects

relatively little is understood about the functional relevance of the endocannabinoid

system. Hence tests or animal models that best reflect this relation can give a better

85

insight into the contribution and relevance of the endocannabinoid system in THC

induced dependence. The effect parameters used in this study were the ICSS

thresholds after exposure to cannabis smoke containing THC, i.e the change from

baseline in the ICSS thresholds. Most of the CNS active drugs have shown to induce

change from baseline threshold in this animal model which indicative hedonia or

anheodina. In case of drugs with an abuse potential, the ICSS thresholds in animals

after drug administration will be lower than the baseline value and the animal will self-

stimulate for currents at which it was previously unresponsive. This change in response

after drug administration can suggests an increase in the sensitivity of the underlying

neuronal mechanism (in brain reward pathway) and can be directly related to hedonic

effects of the drug(Kornetsky et al., 1979). As suggested in the literature the “high”

which is experienced by the subjects usually lags behind the plasma concentrations and

typically last for hours even when there is little or no THC detected in plasma (Chiang

and Barnett, 1984; Ohlsson et al., 1980; Perez-Reyes et al., 1982). To relate the

changes in THC concentrations to changes in affective dependence various structurally

different PKPD models were evaluated and since we were more interested in the basic

model structure mean data could provide true profile pattern, which was used for model

fitting. NONMEM software version 7.0 was used for the purpose of model fitting and

goodness of fit plots, objective function values were evaluated during model

development.

PD Data Smoothing

Any time series is usually associated with some pattern, which is the combination

of an underlying pattern and some random error. The goals is separate this random

error and the underlying pattern. Many method have been suggest such as moving

86

averages, linear regression with time, exponential sequencing etc. Exponential

smoothing is method or simple exponential smoothing is technique where predictions

are made based on the past and the current data. Smoothing is performed by assigning

exponentially decreasing weights as the observations keep getting older in the time

series. This is based on the assumption that the data will typically fluctuate around a

reasonably stable mean with no pattern for growth or no trend.

The equation used to calculate is:

𝑆(𝑡) = 𝛼 ∗ 𝑋(𝑡) + (1 − 𝛼) ∗ 𝑠(𝑡 − 1) (5-1)

An alpha value close to 1 would be close to completely ignoring the previous

observations and if it is close to zero, then the current observation is given much less

weight. The value of St plays an important role and it can either be the first observation

or the mean value of all the observations. The error measure mean squared error

(MSE) and mean absolute percentage error (MAPE) was used for model validation.

There compartment PK with-effect-compartment indirect-link model (biophase equilibration model with a linear PD model)

As shown in Figure 5-2, this model describes the dissociation between affective

dependence and plasma drug concentration with a biophase (i.e., the site of action)

distribution. Drug distribution to the effect site was characterized on the basis of a

hypothetical effect compartment model. Specifically, the rates of the changes in the

drug concentrations are described by the following differential equations:

𝑑(𝐶1)/𝑑𝑡 = −(𝐶𝐿 + 𝑄2 + 𝑄3) ∗ 𝐶1 + 𝐶2 ∗ 𝑄2 + 𝐶3 ∗ 𝑄3

𝑑(𝐶2)/𝑑𝑡 = (𝐶1 − 𝐶2) ∗ 𝑄2

𝑑(𝐶3)/𝑑𝑡 = (𝐶1 − 𝐶3) ∗ 𝑄3

(5-2)

(5-3)

(5-4)

87

𝑑(𝐶𝑒)/𝑑𝑡 = (𝐶1 − 𝐶𝑒) ∗ 𝑘𝑒0

(5-5)

Where ke0 is the first order elimination rate constant for the effect compartment,

C1,C2 and C3 are the concentration is in the central and 2 peripheral compartments

describing the pharmacokinetics of THC, Q2 and Q3 are the intercompartment

clearances. The addition of a hypothetical effect compartment will not alter the

pharmacokinetics of THC as the volume of this compartment is assumed to be

negligible. The final affective dependence which is induced by THC is characterized

using an Sigmoid Eman model using Equation:

𝐸 = 𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (𝐸0) − 𝐸𝑚𝑎𝑥 ∗ (𝐶𝑒 ∗∗ (ℽ))/(𝐸𝐶50 ∗∗ (ℽ) + 𝐶𝐸 ∗∗ (ℽ))

𝐸 = 𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (𝐸0) − 𝑆𝐿𝑂𝑃𝐸 ∗ 𝐶𝑒 ∗∗ (ℽ)

(5-6)

(5-7)

Where E0 represents the baseline response without the drug presence and is set

100 and the Emax is the maximum activity of the drug and EC50 is the concentrations

required to achieve half maximal effect and Ce is the concertation in the hypothetical

effect compartment which is responsible for the pharmacological action of the drug.

When the concentrations of the drug are relatively low comparted to the EC50 value of

the drug a linear model can be used (Figure 5-5). ℽ is the parameter which is used to

describe the steepness of the pharmacological action and is called as the hill factor.

There compartment PK with indirect response model using stimulation of the output (IDR-4)

The indirect response model which is shown in Figure 5-3 describes the

dissociation between affective dependence and hedonic effects via a hypothetical

neurotransmitter. Drugs can either stimulate or inhibit the synthesis of this and the

stimulation of this neurotransmitters will lower the amount of electrical stimulation which

88

is required to maintain minimum response in the ICSS paradigm. Specifically the rate of

change in the hypothetical neurotransmitter and the plasma concentration is described

by the following equations:

𝑑(𝐶1)/𝑑𝑡 = −(𝐶𝐿 + 𝑄2 + 𝑄3) ∗ 𝐶1 + 𝐶2 ∗ 𝑄2 + 𝐶3 ∗ 𝑄3

𝑑(𝐶2)/𝑑𝑡 = (𝐶1 − 𝐶2) ∗ 𝑄2

𝑑(𝐶3)/𝑑𝑡 = (𝐶1 − 𝐶3) ∗ 𝑄3

𝑑(𝑅)

𝑑𝑡= 𝐾𝑖𝑛 − 𝐾𝑜𝑢𝑡 ∗ 𝑅0

(5-8)

(5-9)

(5-10)

(5-11)

Where C1,C2 and C3 are the concentration is in the central and 2 peripheral

compartments describing the pharmacokinetics of THC, Q2 and Q3 are the

intercompartment clearances. Where zero Kin is the zero order constant (electrical

stimulation) and Kout is the first order production and R0 is the baseline intrinsic

response without the drug presence. Kout is the estimated parameters for these models

𝐾𝑖𝑛 = 𝐾𝑜𝑢𝑡 ∗ 𝑅0

𝑅0 =𝐾𝑖𝑛

𝐾𝑜𝑢𝑡= 100 𝑎𝑡 𝑠𝑡𝑒𝑎𝑑𝑦 𝑠𝑡𝑎𝑡𝑒, 𝑡 = 0

(5-12)

(5-13)

Kout is stimulated in the presence of THC leading to a decrease in the amount of

electrical stimulation as a result of which there is a decrease from baseline current.

Hence response in present of the drug is:

𝐸 = 𝐸𝑚𝑎𝑥 ∗ (𝐶𝑝 ∗∗ (ℽ))/(𝐸𝐶50 ∗∗ (ℽ) + 𝐶𝑝 ∗∗ (ℽ))

𝐸 = 𝑆𝐿𝑂𝑃𝐸 ∗ 𝐶𝑝 ∗∗ (ℽ)

𝑑(𝑅)

𝑑𝑡= 𝐾𝑖𝑛 − 𝐾𝑜𝑢𝑡 ∗ 𝑅0 ∗ (1 + 𝐸)

(5-14)

(5-15)

(5-16)

89

Results

The observed plasma concentration-time course for THC can be described by three

compartment pk model constructed in chapter 3. The population mean parameters were

used to and based on this pk model the affective dependence was quantitatively

investigated. The effect compartment model described the data best. Figure 5-5 shows

the observed vs model predictions to characterize THC induced dependence. Table 5-1

shows the summary of the PK/PD parameters. Results suggests an increase in amount

of hedonia experienced in a dose dependent way (for the given dose range of 0.05-0.2

mg/kg) with the 0.2 mg/kg dose producing greater decrease from baseline threshold

current and that there is delayed distribution of THC into the effect compartment and the

hedonic effects can be experienced for prolonged periods (equilibration half-life around

20 hours and hill factor of 0.3). The indirect response model was also able to account

for the delay but could not capture the step decline after the first smoke exposure

session. This suggest that the hedonic feeling experienced by the subjects is immediate

and returns to baseline (gradually) upon stopping drug administration. The goodness of

fit plots for the model are presented in Figure 5-6.

Discussion

THC is the psychoactive cannabinoid whose effect have been well characterized.

However smoking remains the primary route of consumption in humans and the

pharmacodynamic effects of THC are yet to be fully understood in this setting. It was

first suggest by Hollister et al that plasma concentrations might not be the best predictor

for the pharmacological effect after smoking cannabis cigarettes containing 19 mg

THC(Hollister, 1981). The conversion of THC to active metabolite further complicates

90

the understanding of the hedonic effects which are being produced after smoking or oral

administration.

Various different structural model have been proposed to describe the

pharmacokinetics of THC. These include both 2 and 3 compartment disposition model

for intravenous, oral and intrapulmonary administration, as thc readily gets distributed

into the highly perfused organs followed by prolonged storage in adipose

tissue(Heuberger et al., 2015). The plasma or serum concentration of THC has shown

to increase in a dose dependent manner and so do the pharmacological effects(Hunault

et al., 2014).

A recent attempt to characterize the PK/PD relation of subject high which is

experienced after smoking was done by (Strougo et al., 2008) where THC was

administered intrapulmonary using a volcano vaporizer. THC was administered in

increasing doses and both central effects and peripheral effect (cardiovascular) were

assessed using VAS’s scales. It was seen that the subjective high on the VAS rating

increased with increasing doses of THC which was delivered. (Strougo et al., 2008) and

colleges used a two compartment disposition model to characterize the

pharmacokinetics of THC and hypothetical effect compartment model to relate the

plasma concentrations of THC to reported subjective high using VAS scales. The

pharmacological actions in this study were assumed to be purely by THC as the ratio of

metabolite/thc was very low after intrapulmonary administration. The pharmacokinetics

of THC in this study were modelled as an IV bolus admiration as the authors propose

that lung is highly perfused organ and concentrations of THC in plasma are detected

immediately after the first puff suggesting no delay in the absorption. Recently (Awasthi

91

et al., 2018) related the psychoactive effects to the plasma concentrations of THC

using an effect compartment approach. The pharmacokinetics of THC was explained

using three compartment disposition model and the contribution of both the parent drug

and the active metabolite were included to characterize the observed psychotopic

effects after intravenous admisntration of THC. Awasthi and colleges used a

competitive interaction model on the same receptor where both the parent and the

metabolite (which was assumed to be equipotent to THC) act together to produce the

pharmacological effect. Equation shows the simplified version of this approach:

𝐸(𝑡𝑜𝑡𝑎𝑙) = 𝐸𝑚𝑎𝑥(𝑡𝑜𝑡𝑎𝑙) ∗𝐶(𝑇𝐻𝐶) + 𝐶(𝑇𝐻𝐶 − 𝑂𝐻)

𝐸𝐶50 + (𝐶(𝑇𝐻𝐶) + 𝐶(𝑇𝐻𝐶 − 𝑂𝐻)

(5-17)

In the present study we used similar approaches to understand the extent of

hedonic effect which are being produced by THC was exposure to cannabis smoke

containing THC.

After development and validation of THC pk model the exact study dosing

regimen was used to understand the pharmacodynamic effects. Various outcomes have

different equilibration half lives indicating that different systems which are involved with

the production of response, an equilibration half-life of about 20 hours was calculated

based on the estimated parameters. This can be attributed to the lipophilicity of the drug

and the location of CB1 receptors. THC being a lipophilic compound can cross the

blood brain barrier and can get accumulated in the brain for long durations. This

suggest that the hedonic effects of THC can be present even with no drug detected in

plasma after single dose administration. Since the ICSS paradigm assumes synergy

92

between the electrical stimulation and drug, it can be said that THC dose dependably

increase the amount of dopamerginic neuronal firing leading to stable responses even

at lower current threshold. This is in agreement with the reported literature where DA

administered in directly in NA and VTA caused an increase in the amount of

extracellular dopamine(Chen et al., 1993, 1991) and it was suggested that THC

administration increases the dopamine neuronal firing rates and increase the synthesis

of dopamine. Other rodent studies which tried to observe the somatic withdrawal signs

in rats exposed to cannabis smoke containing THC also observed a clear change in the

somatic scores in the animals 4 hours after smoke exposure, further providing evidence

that the hedonic effects of cannabis are developed slowly and last for a long duration

because anhedonia in these rodents were studied 4 hours later(Bruijnzeel et al., 2016).

Another recent study also showed similar results where subject smoked cannabis

cigarettes containing from 0-23% THC and the reported euphoric effect were dose

dependent and could be felt until 5-6 hours after completion of the joint. And the

reported high lasted longer durations with higher doses of THC was inhalation i.e

cigarettes containing more amount THC were smoked by the subjects(Hunault et al.,

2014). Imaging studies further showed the there is decreased dopamine brain activity in

chronic cannabis users which is associated with anhedonia(Volkow et al., 2014)

suggesting the involvement of dopamine in THC mediated rewarding effects.

One disadvantage with this approach is that the placebo data was not modelled

since no specific patterns were observed over the time course to be able to use a

mathematical function to capture that trend. Hence we lose the power to simulate

placebo response for future studies in order to make a direct comparison between the

93

placebo/vehicle treated subjects vs drug treated subjects in the ICSS paradigm. Another

drawback is that, based on the model prediction the hedonic effects can last up to 6

hours but we had only one PD data point per dose administered at each dose taken

around 30-45 min after cannabis smoke exposure, which might be an under prediction

of the true effect leading to bias when simulating with entirely different trial design or

dosing regimens.

The developed PK-PD model used a 3 compartment disposition model for

pharmacokinetics similar pharmacokinetic models proposed for humans and is useful

during the development of novel compounds with similar underlying mechanisms, such

that concentration-effect relationships of other agonists can be compared to THC, using

THC as the “gold standard”. In the present study we tried to understand the effect of

cannabis smoke exposure on hedonia which is experienced after inhalation but various

other end points could also be assessed. Finally, PK-PD models may enable prediction

of the time course of the analyzed effects, after co-administration of THC and antagonist

leads in pharmacological proof-of-mechanism counteraction studies. With all these

features, this integrated modelling approach offers insights into underlying mechanism

of the endocannabinoid system. In this way, PK-PD modelling can contribute to the

development of novel cannabinoid agonist, antagonists and eventually lead to the

development of therapies to treat cannabis use disorders (CUD’s).

94

Figure 5-1. Placebo corrected PD response

95

Figure 5-2. PK-PD model using hypothetical effect compartment

Figure 5-3. PK-PD model using indirect response model (IDR4)

96

Figure 5-4. Linear Emax model

Figure 5-5. Observed vs model prediction for THC induced cannabinoid dependence

97

Figure 5-6. GOF’s PD Model

98

Table 5-1. Summary ok the PD parameters obtained after model fitting

Parameter Estimate RSE %

Ke0 (h-1

) 0.03 34

E0 100 FIXED -

SLOPE 19.1 9.3

GAMMA 0.23 2

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CHAPTER 6 CONCLUSION

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V

2013), now recognizes cannabis abuse, withdrawal and other cannabis related

disorders under cannabis use disorders. In the United states alone about 845,000

people were treated for CUD’s and this number being higher than other drugs of abuse

and currently there are no therapies approved by the FDA for treatment and relapse

rates with motivation therapy or family based therapies are high (greater than 70%

relapse rates). THC which the psychoactive constituent of marijuana is a lipophilic

molecule and is known to have a prolonged half-life. Previous studies done in rodents to

characterize the acute effects of THC used parenteral administration of the compound

which in reality might not reflect the true effects after smoking cannabis. At present

about 20 states have legalized cannabis for medicinal purpose and 10 states have

legalized it for recreational purposes Hence with these ongoing efforts to legalize

marijuana it is although more important to understand the underlying mechanism of

cannabinoid dependence and withdrawal. Understanding dependence is a complex

process which has to be finally evaluated and tested in clinic but establishing an animal

model to study these effects can be useful to address questions prior to testing in

clinical setting. The overall objective of the thesis was to study thc induced cannabinoid

dependence and withdrawal mechanisms after exposure to cannabis smoke exposure.

Highly sensitive LCMS method was developed in rat plasma to quantify, THC,

CBD and Rimoanabant. The method was then validated in terms of parameters like

sensitivity, accuracy, stability etc and other parameters which are given in the USFDA

guidance for bioanalytical method validation for the industry (chapter 2).The fully

100

developed and validated method was then successfully applied to understand the

exposure of THC after cannabis smoke inhalation and intravenous administration

(chapter 3). We tried to evaluate the effect of dose and treatment duration on

development of cannabinoid dependence and results from (chapter 4) have shown thc

induced affective dependence as seen by rimonabant precipitated withdrawal. A dose

dependent elevations in thresholds were observed upon administration of the CB1

receptor antagonist.

In conclusion, these studies demonstrated the connections among thc plasma

levels, drug-exposure duration and the development of physical dependence. Finally

PK/PD model (chapter 5) was built to characterize the affective dependence after

cannabis smoke exposure, which suggested that the hedonic effects produced by THC

are mild (for the given doses in the study) and will increase in a dose dependent

manner and will last for prolonged durations. The PK/PD model developed in these

studies were able to describe the overall time courses of affective dependence. We

expect that the modeling exercises presented will profitably contribute to the formulation

of hypotheses of physical dependence. From a clinical perspective, these studies

suggest that: 1) When cannabis is smoked the development of dependence is slow; 2)

increasing the treatment period does not further increase the hedonic effects as these

are dose dependent which mean high potency cannabis products can induce greater

dependency; and 3) the withdrawal associated with cannabinoid abuse is very sharp,

which eventually leads to drugs seeking behavior and relapse.

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BIOGRAPHICAL SKETCH

Abhigyan Ravula was born in Hyderabad, India in 1992. In 2013, he received his

bachelor’s in pharmacy from the Manipal College of Pharmaceutical Sciences, Manipal,

India. In August 2013, he began his graduate studies in the Department of

Pharmaceutics at the University of Florida, Gainesville under the direction of Dr.

Hartmut Derendorf. His research interests include POPPK, PBPK and PK/PD modeling

of cannabinoids. In his dissertation, Abhigyan focuses on evaluating the

pharmacokinetics and pharmacodynamics of tetrahydrocannabinol to characterize

tetrahydrocannabinol induced cannabinoid dependence. In addition, Abhigyan gained

industry experience as an Academic Contractor in the Global PK/PD and

Pharmacometrics group at Eli Lilly. He gave several poster presentations at various

international conferences. He is an active member of ACCP. Abhigyan was also a

member of the University of Florida and South East All Stars (American College Cricket,

2018) Cricket Team. He received his Ph.D. from University of Florida in the spring of

2018.