Prediction of THC Plasma and Brain Concentrations following. Marijuana Administration: Approach and Challenges

Transcription

1 Prediction of THC Plasma and Brain Concentrations following Marijuana Administration: Approach and Challenges Contents: 1. Background and Significance 1.1. Introduction 1.2. THC - the primary chemical constituent of interest 1.3. Prediction of plasma and brain concentrations in human: Challenges and Approach 1.4. Modeling and Simulations: Importance in Clinical Pharmacology 1.5. Physiologically-based pharmacokinetic model (PBPK) to estimate the plasma and tissue concentration of THC 2. Objectives 2.1. Specific aim and hypothesis 3. Research Strategy and Preliminary Investigation 3.1. PBPK model development tool 3.2. Tissue compartments in PBPK model 4. Results and Discussion 4.1. PBPK model evaluation 4.2. Prediction of human plasma and brain concentrations of THC 5. Future Work 6. Appendix (Figures) 7. References 1

2 1. Background and Significance 1.1. Introduction The National Institute on Drug Abuse (NIDA) identifies marijuana as the most commonly used illicit drug in the United States; it is also one of the most commonly abused drugs throughout the world (1). Smoking (intrapulmonary delivery route) is the preferred mode of marijuana administration for both medicinal and recreational users. The increasing interest in the use of medical marijuana and the movement toward the legalization of recreational marijuana use create an increased need to better understand the pharmacokinetics of the active components of marijuana and their resulting pharmacodynamic/pharmacologic effects. Pharmacokinetics (PK) is the study of the time course of the absorption, distribution, metabolism, and excretion of a drug and is often evaluated using blood/plasma concentrations. Pharmacodynamics (PD) is the study of the relationship between the concentration of drug at the site of action (biophase compartment) and the exerted effect. The purpose of this research is to develop a physiologically-based pharmacokinetic model capable of predicting drug concentrations at sites not directly accessible for sampling followed by developing correlations of the simulated concentrations to clinically observed pharmacological effects THC - the primary chemical constituent of interest Cannabis is composed of a vast number of chemical constituents, with Δ 9 -tetrahydrocannabinol (THC) being the principal mediator of the observed psychoactive and cardiovascular effects (2). THC, once administered, rapidly distributes to highly vascularized tissues, including the brain (3). Because of their lipophilic natures, THC and its metabolites (primary metabolite, 11- hydroxy-thc (THC-OH); secondary metabolite, 11-nor-9-carboxy- Δ 9 -THC (THC-COOH)) 2

3 readily enter less vascularized tissues, where significant concentrations may remain for extended time periods (3,4) Prediction of plasma and brain concentrations in humans: Approach and Challenges THC, Δ 9 -tetrahydrocannabinol, the primary active chemical constituent in marijuana following marijuana smoking is known to produce psychoactive effects. THC is rapidly cleared from the bloodstream, and plasma concentrations cannot be used to directly predict the observed psychoactive effects. Johansson et al. hypothesized that residual levels of THC in the accumulating tissues (e.g. brain), long after the time of administration, might be responsible for the longer-term observed psychoactive effects (5). Hence, it is imperative to be able to predict the concentration of THC in the human brain in order to explain and predict the potential physical and behavioral effects. Since, in most cases, the brain is inaccessible for direct sampling, other methods which allow for the prediction of the brain concentration-time profile need to be developed. Further efforts utilizing modeling and simulation approaches, can be used to predict brain concentrations and correlate those to the observed physiologic effects to better quantify the risks associated with marijuana use. The use of modeling and simulation approaches may successfully predict drug concentrations in otherwise difficult-to-sample anatomical regions Modeling and Simulations: Importance in Clinical Pharmacology With the advancement in computing technologies and their application in the fields of clinical pharmacology and clinical pharmacokinetics, computer-based simulations have evolved as a tool for the prediction of the effects of drugs in physiological systems. In addition to the advantages 3

4 of being less expensive and less labor intensive compared to animal experiments or trials using human subjects, simulations can provide information that is experimentally difficult to obtain. Physiologically-Based Pharmacokinetic Modeling (PBPK) is a computational technique which uses known anatomic and physiologic characteristics, such as organ weight/volume, regional blood flow, and metabolism patterns, to quantitatively describe drug distribution patterns within the body. In these studies, PBPK techniques, along with known THC chemical and biochemical characteristics, are being used to predict brain THC concentrations. Model building was initiated using reported data from a controlled clinical trial which administered THC intravenously to a group of adult males (6) Physiologically-based pharmacokinetic models (PBPK) to estimate the plasma and tissue concentration of THC A PBPK model is a multi-compartment model with each compartment assigned to a specific organ or tissue. These compartments are connected in a flow model using the blood supply (circulatory system) as the system input and output source (7). PBPK modeling is an example of a bottom-up modeling approach wherein the compartmental structure derived using known system (anatomical) characteristics and parameterized using the experimentally determined physicochemical properties of the drug is utilized to predict the concentration-time profile in plasma and other tissues. Given a known input concentration of the drug (the dose), the resulting concentration-time profile of the drug in plasma and other tissues can then be simulated. The strength of the PBPK modeling approach lies in its application to the prediction of drug concentrations in inaccessible tissues; these methods have been widely used in the field of toxicology (8). PBPK models are finding increasing use in drug exposure risk assessment analyses due to the capability of such models to predict age- and gender-specific 4

5 pharmacokinetics for the populations that are difficult to study experimentally (e.g. pediatrics, pregnant women) (9). Frequently, PBPK models are derived and validated based on data obtained from animal species where both blood and tissue sampling may be possible. Interspecies scaling, an extrapolation between animal species and humans that accounts for differences in physiology, can then be applied to enable the prediction of a human profile from preclinical animal data without the necessity for human testing. 2. Objectives: The overall objective of this research is to use modeling-based approaches to better understand the time-dependent relationships between the dose of THC administered following intrapulmonary administration (smoking) and resulting physiologic effects Specific Aim: To develop a PBPK model to estimate the plasma and tissue concentrations of THC following marijuana administration The working hypothesis of this aim is that the concentration-time profile of THC in plasma and in tissue compartments can be predicted using a PBPK modeling approach. The following activities were conducted to initiate model development: a) Develop a human PBPK model using known physiological system parameters and the known physicochemical properties of THC using the PBPKPlus module in GastroPlus TM (Version 8.5, Simulations Plus, Inc., Lancaster, CA). GastroPlus TM is a modeling and simulation software package widely used by pharmaceutical scientists and the US Food and Drug Administration (FDA) for decision-making in drug discovery and development. Model diagnostics and optimization were performed by comparing the predicted (simulated) plasma profile obtained from the model to observed profiles obtained from the literature. 5

6 b) Prediction of THC profile in brain: The concentration-time profile for THC in the brain, the tissue of major interest regarding THC s psychoactive effects, is estimated using the PBPK model. 3. Research strategy and preliminary investigation: The PBPK model will allow the simulation of the effect of varying marijuana/thc dose and population physiology on the concentration-time profile of THC in plasma and other tissues. Development of a PBPK model to estimate the plasma and tissue concentrations of THC following THC administration 3.1. PBPK model development tool: The biopharmaceutical properties of THC were obtained from the literature, and, for those values that were not available, estimates were made using ADMET TM Predictor Version 6.5 (Simulations Plus, Inc., Lancaster, CA). The PBPKPlus TM module works in conjunction with an internal module named PEAR Physiology (Population Estimates for Age-Related Physiology). PEAR enables the construction of a physiological system with specific age, body weight, and race (limited to Western and Asian) features. The whole-body physiological model (Figure 2A) consists of selected tissue compartments (organs) connected via the circulatory system (blood) with the amount of drug in each compartment governed by a unique set of differential equations Tissue compartments in PBPK model: Each tissue compartment in the PBPKPlus TM module can be individually designated as either a Perfusion-limited or a Permeabilitylimited region. Selection of a perfusion-limited tissue compartment is appropriate when the permeability/ uptake of the drug into the tissue is rapid and the transfer of drug into the tissue is governed by the perfusion/ input rate (blood flow). Permeability-limited tissues, in contrast, are those where the transfer of drug to the tissue is low, and uptake of the drug into the tissue is 6

7 limited by the permeability (partitioning) properties of the drug and the total surface area of the tissue exposed to the drug input from the bloodstream. For THC, all tissue compartments in the PBPK model were assumed to be perfusion-limited (Figure 2B) due to its high log P (logarithm of partition-coefficient; high log P indicates high permeability in tissues) value. The mass balance differential equation for a non-eliminating, perfusion-limited tissue is described in the whole body PBPK model by (7,10): ( ) Eq. 1 where and refer to the volume of the tissue (organ), concentration of drug in the tissue, blood flow to the tissue, concentration of drug coming into the tissue (arterial), and the tissue-plasma partition coefficient for tissue i, respectively. Similarly, for eliminating/clearance tissues like the liver, the mass balance differential equation describing its drug tissue concentration is as follows: ( ( )) ( ) Eq. 2 where and are the intrinsic clearance and fraction of the unbound drug in plasma, respectively. The Rodgers and Rowland method (11) was selected for the calculation of the tissue-to-plasma partition coefficient (K p ) values in the PBPK model. This method encompasses a number of processes, including the drug dissolution rate in tissue water, partitioning of drug into neutral lipids and phospholipids, and the distribution process related to the ionization of the drug. 7

8 4. Results and Discussion 4.1. PBPK model evaluation A diagnostic evaluation of the model was performed by selecting a set of physiological conditions consistent with the population used for the investigation of intravenously administered THC pharmacokinetics described in Tseng et al. (6). The comparison was based on the average conditions (average dose = 4mg) in males (N=6, age = 23 ± 3 yr, weight = 72 ± 9 kg) receiving a THC infusion over an average interval of 20 minutes. The predicted and observed plasma concentration-time profiles (Figure 3) show good agreement, thereby supporting the predictive ability of the PBPK model. Additional populations and datasets will need to be evaluated to expand the potential utility of the model Prediction of human plasma and brain concentrations of THC Because of the critical importance of the psychoactive effects of THC, coupled with the inability to directly measure brain concentrations, the PBPK model was used to predict the concentration of THC in plasma and in the brain. Figure 4 shows a simulated concentration-time profile for THC in both plasma and brain using the initial PBPK model. The simulated peak THC concentration in brain was observed to be nearly four-fold higher that in the plasma, which supports the need to link the brain as the biophase compartment when studying the psychoactive effects elicited by THC. The brain concentration-time profile clearly shows higher terminal levels of THC (~50 ng/ml), compared to very low concentrations of THC in the plasma at the same time points. Since the concentration of drug in the brain likely determines the observed effects, the observed longer-term psychoactive effects of THC could be explained by the high THC concentrations predicted in the brain. 8

9 5. Future Work The focus of this research with regard to PBPK modeling has been to develop a basic model that predicts the plasma and the brain concentration profiles for THC. This model will continue to be optimized and will be extended to predict the effects of changes in physiology, e.g. gender, age, body weight, on plasma and tissue concentration-time profiles. In addition, the effect of the route of administration of THC, including the intrapulmonary route of administration (smoking) and the oral route (through the gastrointestinal tract), on the concentration-time profile in plasma and tissues will be examined. 9

10 6. Appendix: Figures THC in human brain Psychoactive Effects Marijuana Figure 1. Marijuana smoking and psychoactive effects resulting from the concentration of THC in the brain (Images: Microsoft PowerPoint Clip Art). Q C bi Plasma (V p, C p, f up ) C bo Q Tissue (V t, C t, f ut, CL int ) Figure 2A Figure 2B Figure 2: Whole-body PBPK model with various organs connected by circulatory system (2A). Diagrammatic representation of a perfusion-limited tissue model (2B). Q is the blood flow in and out of the tissue. V, C, and f u are the volume, concentration, and fraction of unbound drug, respectively. C bi and C bo are the concentration of drug in the blood moving into the tissue (arterial) and concentration of drug in the blood moving out (venous) from the tissue compartment. 10

11 Figure 3A Figure 3B Figure 3: PBPK Model Diagnosis: Comparison of predicted plasma concentration-time profile with observed data (6) using PBPKPlus TM module. Red line, predicted (simulated) data; blue squares, observed data (average) reported by Tseng et al. (6). Fig. 3B: semi-log plot with concentration on log scale for better visual inspection of the terminal phase of the plot. THC Profile in Brain THC Profile in Plasma Figure 4: Simulation of brain THC profile: Simulated brain (purple line) and plasma (blue line) profile using PBPKPlus TM. 11

12 7. References 1. Substance Abuse and Mental Health Services Administration. Results from the 2010 National Survey on Drug Use and Health: Summary of National Findings, HHS Pub. No. (SMA) , Rockville, MD: SAMHSA, E.L. Karschner, E.W. Schwilke, R.H. Lowe, W.D. Darwin, R.I. Herning, J.L. Cadet, and M.A. Huestis. Implications of plasma Delta9-tetrahydrocannabinol, 11-hydroxy-THC, and 11-nor-9- carboxy-thc concentrations in chronic cannabis smokers. Journal of analytical toxicology. 33: (2009). 3. C.C. Hunault, J.C. van Eijkeren, T.T. Mensinga, I. de Vries, M.E. Leenders, and J. Meulenbelt. Disposition of smoked cannabis with high Delta(9)-tetrahydrocannabinol content: A kinetic model. Toxicology and applied pharmacology (2010). 4. M.A. Huestis, J.M. Mitchell, and E.J. Cone. Urinary excretion profiles of 11-nor-9-carboxy-delta 9-tetrahydrocannabinol in humans after single smoked doses of marijuana. Journal of analytical toxicology. 20: (1996). 5. E. Johansson, M.M. Halldin, S. Agurell, L.E. Hollister, and H.K. Gillespie. Terminal elimination plasma half-life of delta 1-tetrahydrocannabinol (delta 1-THC) in heavy users of marijuana. European journal of clinical pharmacology. 37: (1989). 6. A.H. Tseng, J.W. Harding, and R.M. Craft. Pharmacokinetic factors in sex differences in Delta 9- tetrahydrocannabinol-induced behavioral effects in rats. Behavioural brain research. 154:77-83 (2004). 7. H.M. Jones, I.B. Gardner, and K.J. Watson. Modelling and PBPK simulation in drug discovery. The AAPS journal. 11: (2009). 8. N. Tsamandouras, A. Rostami-Hodjegan, and L. Aarons. Combining the "bottom-up" and "topdown" approaches in pharmacokinetic modelling: Fitting PBPK models to observed clinical data. British journal of clinical pharmacology (2013). 9. H.J. Clewell, P.R. Gentry, T.R. Covington, R. Sarangapani, and J.G. Teeguarden. Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry. Toxicological sciences : an official journal of the Society of Toxicology. 79: (2004). 10. B. Xia, T. Heimbach, T.H. Lin, H. He, Y. Wang, and E. Tan. Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions. Cancer chemotherapy and pharmacology. 69: (2012). 11. T. Rodgersand M. Rowland. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. Journal of pharmaceutical sciences. 95: (2006). 12